Senior Data Engineer, MLOps - Remote Opportunity

Raft is seeking a Principal MLOps Engineer to help build and scale advanced AI and data platforms for the Department of Defense. This position plays a key role in transforming large volumes of real-time sensor and operational data into actionable intelligence, supporting critical decision-making for operators through mission applications and operational disp…

Location: Remote (U.S.); DMV; McLean, VA; Boston, MA; San Antonio, TX; Colorado Springs, CO; Tampa, FL; Honolulu, HI Eligibility: U.S. citizenship required. All work must be performed within the continental United States. Raft delivers AI, machine learning, and data-driven solutions to U.S. military and government clients. The team specializes in autonomous data fusion, Agentic AI, distributed data systems, and building scalable platforms. Headquartered in McLean, VA, Raft partners with federal and public sector organizations to create digital solutions that reach millions of Americans, using design thinking and cloud-native technology. Role overview The Lead Principal MLOps Engineer plays a key part in supporting clients and collaborating with a team focused on complex, high-impact challenges. This position centers on building and maintaining mission-critical AI and data platforms for the Department of Defense. What you will do Work on platforms that process large volumes of real-time data from diverse sensors and operational sources. Transform raw data into actionable intelligence, supporting operators with timely decisions through mission applications and operational dashboards. Support a large-scale platform that manages billions of events daily, using low-latency data pipelines and cloud-native infrastructure. Advance the development of an end-to-end machine learning platform for model development, evaluation, deployment, monitoring, and lifecycle management across both cloud and constrained operational environments.

Location: Remote (U.S. only), DMV, McLean, VA, Boston, MA, San Antonio, TX, Colorado Springs, CO, Tampa, FL, Honolulu, HI Clearance: U.S. citizenship required. All work must be performed within the continental U.S. Role overview The Principal MLOps Engineer will work with a team dedicated to building AI and data platforms for the Department of Defense. These platforms manage and process large streams of real-time data collected from various sensors and operational sources. The objective is to turn raw data into actionable intelligence, supporting operators with applications that enable fast, informed decisions. Raft’s systems run at scale, processing billions of events each day through low-latency data pipelines and cloud-native infrastructure. As AI capabilities grow, the engineering team is focused on expanding a comprehensive machine learning platform. This platform covers the full lifecycle: model development, evaluation, deployment, monitoring, and ongoing management, both in the cloud and in operational environments with limited resources. About Raft Raft is a defense technology company that supports U.S. military and government agencies with advanced AI/ML and data solutions. The team specializes in autonomous data fusion, Agentic AI, distributed data systems, scalable platforms, and complex application development. Based in McLean, VA, Raft partners with federal and public sector clients, using design thinking and cloud-native technology to deliver digital solutions that reach millions of Americans.

Hayden AI builds mobile perception systems that help transit agencies and city governments address real-world challenges. The team focuses on computer vision to improve bus lane and bus stop enforcement, modernize transportation technology, and support safer, more sustainable streets. The MLOps Engineer will join the Perception Deep Learning team in San Francisco, working in a hybrid model (at least three days per week in the office). This role centers on building and advancing Hayden AI’s machine learning platform, collaborating with perception, deep learning, and platform engineers to create infrastructure for training and deploying ML models. The position involves shaping the architecture for data ingestion, training pipelines, deployment, monitoring, and governance. What you will do Design, implement, and maintain cloud-based workflows for deploying and managing AI models. Work with cross-functional teams to identify workflow bottlenecks and deliver solutions that improve efficiency. Deploy new features and updates quickly while maintaining quality and reliability, and apply cost-saving strategies to optimize infrastructure spending. Stay up to date with new MLOps tools and technologies, integrating them to improve ML workflows. Participate in the team’s software development process, including design and code reviews, brainstorming, and maintaining accurate documentation. Requirements Bachelor’s degree and 3-4 years of experience in a related field.

Field AI is revolutionizing the interaction between robots and the physical world. Our mission is to create AI systems that are risk-aware, dependable, and ready for real-world applications, tackling the intricate challenges of robotics to unlock the true potential of embodied intelligence. By moving beyond standard data-driven methods and purely transformer-based models, we are pioneering innovative solutions that are already deployed globally, yielding tangible results and continually refining our models through actual field applications.We are on the lookout for a talented and driven MLOps Engineer to bolster our engineering team. In this pivotal role, you will be responsible for designing and maintaining the infrastructure and tools essential for the comprehensive lifecycle of machine learning systems utilized in robotics. Collaborating closely with machine learning engineers, robotics experts, and infrastructure teams, you will ensure the reliable training, evaluation, deployment, and monitoring of ML models. This position presents a thrilling opportunity to operationalize machine learning within dynamic and rapidly evolving robotic systems.

Fortytwo is a cutting-edge decentralized AI protocol built on Monad, which utilizes underused consumer hardware for swarm inference. Our innovative approach allows Small Language Models to execute complex multi-step reasoning at a reduced cost, outperforming the capabilities and scalability of existing leading models.Key Responsibilities:Deploy robust, scalable machine learning services with optimized infrastructure and automated Kubernetes clusters.Enhance GPU resource utilization through Multi-Instance GPU (MIG) and Node Offloading System (NOS).Oversee cloud storage solutions (e.g., S3) to guarantee availability and performance.Incorporate state-of-the-art ML methods, including Low-Rank Adaptation (LoRA) and model merging into operational workflows:Adapt leading ML codebases to align with organizational requirements.Implement LoRA methodologies and model merging processes.Manage and deploy large language models (LLM), small language models (SLM), and large multimodal models (LMM).Utilize technologies such as Triton Inference Server for model serving.Leverage advanced serving frameworks like vLLM and Text Generation Inference (TGI).Optimize models using ONNX and TensorRT to ensure efficient deployment.Develop Retrieval-Augmented Generation (RAG) systems that integrate spreadsheet, mathematics, and compiler processing.Establish monitoring and logging systems with tools such as Grafana, Prometheus, Loki, Elasticsearch, and OpenSearch.Create and maintain CI/CD pipelines via GitHub Actions for streamlined deployment.Develop Helm templates for swift Kubernetes node deployment.Automate workflows using cron jobs and Airflow Directed Acyclic Graphs (DAGs).Qualifications:Bachelor’s or Master’s degree in Computer Science, Engineering, or a related discipline.Expertise in Kubernetes, Helm, and containerization technologies.Experience in GPU optimization (MIG, NOS) and familiarity with cloud platforms (AWS, GCP, Azure).Proficient in monitoring tools (Grafana, Prometheus) and scripting languages (Python, Bash, etc.).

About the RoleJoin our dynamic team at Bonsai Robotics as a Senior MLOps Engineer, where you'll play a pivotal role in shaping the future of robotics. We seek an innovative engineer who excels in real-world environments and can oversee the complete machine learning lifecycle—from data ingestion and labeling to training, evaluation, and performance monitoring. Your contributions will support a sophisticated perception stack that includes 2D/3D object detection, semantic and instance segmentation, depth estimation, and multi-sensor fusion utilizing both camera and lidar technologies.This position is highly collaborative; you will work closely with perception engineers, autonomy specialists, field operations, and external labeling teams. Your work will be fast-paced, impactful, and will influence every vehicle deployed in the field.

At dv01, we are unveiling the intricacies of the world's largest financial market: structured finance. Our platform serves as the backbone for vital activities that promote financial independence, ranging from consolidating credit card debt to refinancing student loans, purchasing homes, and launching small businesses. dv01’s cutting-edge data analytics platform offers unparalleled insights into investment performance and risk for lenders and Wall Street investors involved with structured products. As a data-centric organization, we meticulously manage essential loan data and develop state-of-the-art analytical tools that empower strategic decision-making for responsible lending. In essence, we are committed to preventing a recurrence of the 2008 global financial crisis by providing the data and tools necessary for informed, data-driven decisions that contribute to a safer financial environment for everyone. With over 400 of the largest financial institutions relying on dv01, we cover more than 100 million loans across various sectors, including mortgages, personal loans, auto loans, buy-now-pay-later schemes, small business loans, and student loans. dv01 is continually broadening its market coverage, adding new loans monthly, and innovating new technologies for the structured products landscape.

Contribute to a Safer World.At TRM Labs, we leverage blockchain analytics and artificial intelligence to empower law enforcement, national security agencies, financial institutions, and cryptocurrency enterprises in the fight against crypto-related fraud and financial crime. Our advanced blockchain intelligence and AI platforms are designed to trace transactions, identify illicit activities, build investigative cases, and establish a comprehensive view of potential threats. Trusted by leading organizations worldwide, TRM is committed to fostering a safer, more secure environment for everyone.The AI Engineering Team is dedicated to driving the development of next-generation AI applications, specifically focusing on Large Language Models (LLMs) and agentic systems. Our mission is to create resilient pipelines, high-performance infrastructure, and operational tools that facilitate the swift, safe, and scalable deployment of AI systems.We manage extensive petabyte-scale data pipelines, deliver model outputs with millisecond-level latency, and ensure observability and governance to make AI production-ready. Our team actively evaluates and integrates state-of-the-art tools in the LLM and agent domain, such as open-source stacks, vector databases, evaluation frameworks, and orchestration tools, which enhance TRM's ability to innovate more rapidly than the competition.In the role of Senior MLOps Engineer specializing in LLMOps, you will play a pivotal role in constructing and scaling the technical infrastructure required for AI and ML systems. Responsibilities include:Develop reusable CI/CD workflows for model training, evaluation, and deployment, incorporating tools like Langfuse, GitHub Actions, and experiment tracking.Automate model versioning, approval processes, and compliance checks across various environments.Construct a modular and scalable AI infrastructure stack, integrating vector databases, feature stores, model registries, and observability tools.Collaborate with engineering and data science teams to integrate AI models and agents into real-time applications and workflows.Regularly assess and incorporate cutting-edge AI tools (e.g., LangChain, LlamaIndex, vLLM, MLflow, BentoML, etc.).Enhance AI reliability and governance, promoting experimentation while ensuring compliance, security, and system uptime.Optimize AI/ML model performance by ensuring data accuracy, consistency, and reliability to improve training and inference processes.Deploy infrastructure that supports both offline and online LLM evaluations.

Join Quince as a Senior Engineering Manager specializing in MLOps, where you will lead a talented team in building and maintaining machine learning operations that drive our innovative products. Your expertise will help optimize workflows and ensure the seamless integration of ML models into production environments.

About Us At Hitachi Digital Services, we are pioneers in the realm of digital solutions and transformation. Our vision is to unlock the immense potential of our world, and we are driven by a people-centric approach that aims to create positive change. Every day, we innovate to future-proof urban spaces, conserve natural resources, protect vital ecosystems, and enhance lives. Our unique blend of innovation, technology, and expertise empowers us to lead both our company and clients into the future. We believe that diverse experiences and perspectives are invaluable. We value your character, life experiences, and passion just as much as your qualifications. Join Our Team We are seeking a dedicated MLOps L2 Support Engineer who will play a crucial role in providing 24/7 production support for our machine learning (ML) and data pipelines. This role involves on-call support, including weekends, to ensure the high availability and reliability of our ML workflows. You will work with technologies such as Dataiku, AWS, CI/CD pipelines, and containerized deployments to maintain and troubleshoot ML models in production. Key Responsibilities: Deliver L2 support for MLOps production environments, ensuring maximum uptime and reliability. Troubleshoot issues related to ML pipelines, data processing jobs, and APIs. Monitor logs, alerts, and performance metrics using tools like Dataiku, Prometheus, Grafana, or AWS CloudWatch. Conduct root cause analysis (RCA) and resolve incidents within agreed SLAs. Escalate unresolved issues to L3 engineering teams as necessary. Dataiku Platform Management: Manage Dataiku DSS workflows, troubleshoot job failures, and optimize performance. Monitor and support Dataiku plugins, APIs, and automation scenarios. Collaborate with Data Scientists and Data Engineers to debug ML model deployments. Perform version control and ensure proper documentation.

Logic20/20 is on the lookout for a seasoned Lead MLOps Engineer to spearhead and mentor our dynamic data science teams. This role involves harnessing the power of artificial intelligence and machine learning to enhance computer vision and customer intent models. Join us in this thrilling opportunity to impact the industry by developing production-level systems through the application of advanced machine learning models.Key Responsibilities:Design and architect frameworks to predict diverse outcomes across various scenarios.Build, deploy, and enhance machine learning models to meet production standards.Collaborate closely with fellow data scientists and stakeholders on innovative projects.Develop sophisticated solutions utilizing Python.Ensure the delivery of production-grade solutions.Engage with technologies such as Hadoop, Redshift, and Spark.Translate complex business and product inquiries into actionable analytics projects.Effectively communicate insights derived from complex methodologies through clear written and verbal channels.

Who We Are At PathAI, our mission is to enhance patient outcomes using cutting-edge AI technology in pathology. Our innovative platform significantly boosts diagnostic accuracy and treatment efficacy for illnesses such as cancer by harnessing advanced machine learning and artificial intelligence methodologies. We have a proven history of successfully implementing AI algorithms in histopathology for translational research, pathology laboratories, and clinical trials. Our commitment to rigorous scientific research and careful analysis is fundamental to our operations. Our diverse team is dedicated to tackling complex challenges and making a meaningful difference in patient care. Your Role As the Associate Director of MLOps, you will spearhead the team that underpins our AI/ML infrastructure, bridging the gap between machine learning research and large-scale production. Your primary responsibility will be to enhance our infrastructure to accommodate the growing demands of extensive ML training and inference tasks. You thrive on designing and building systems that emphasize reliability, enjoy collaborating with others, and embrace technical challenges while maintaining a sense of humor. Our technical landscape is extensive, encompassing high-scale AI training and inference workloads, cloud infrastructure, Kubernetes, observability, distributed systems, and various related technologies. Your Responsibilities This position plays a pivotal role in driving the scalability and efficiency of our Machine Learning Operations platform through impactful and strategic initiatives. Vision and Roadmap: Formulate and implement a long-term vision and roadmap for the MLOps team to meet the ML development and deployment requirements of various business units. Successfully navigate the balance between immediate tactical objectives and long-term architectural advancements for future expansion. Team Leadership: Lead and mentor a team of 6-7 high-performing engineers. Strategically assign resources to support existing services while pursuing critical strategic projects. Cross-Functional Collaboration: Collaborate with leaders across machine learning, data science, product engineering, and infrastructure to proactively identify challenges, address bottlenecks, and facilitate the deployment of innovative solutions. Foundation Model Readiness: Design the computational and storage pipelines necessary for ML Engineers to manage millions of slides and intricate derived artifacts without data fragmentation or synchronization delays.

At Opendoor, we are reimagining the real estate industry with cutting-edge technology and innovative practices. We are looking for a talented MLOps Software Engineer specializing in pricing to join our dynamic team in Seattle. In this role, you will leverage your expertise in machine learning operations to enhance our pricing models, ensuring they are robust, scalable, and efficient.As a member of our engineering team, you will collaborate closely with data scientists and product managers to develop solutions that optimize pricing strategies and improve user experiences. We seek forward-thinkers who are passionate about using technology to drive significant business impact.

Fully remote | Complete engagement jobFounded in Palo Alto by Dr. Andrew Ng and Israel Niezen, Factored is dedicated to empowering organizations by assisting U.S. companies in building and scaling elite AI, ML, and Data teams. Our mission is clear: to unleash the potential of exceptional individuals and amplify their impact in the world.As a member of Factored, you will join a vibrant community that champions learning, ownership, and authenticity. We value your growth, embrace your ideas, and foster a culture of transparency, curiosity, and collaboration. Together, we aim for excellence, celebrate diversity, encourage inquiry, and cultivate an environment where you can truly thrive.We are seeking a MLOps Engineer to join our innovative team. In this role, you will be integral to the development and maintenance of AI products for our clients. At Factored, we envision a company that belongs to all of us, and we need your expertise to elevate this journey to new heights and create new opportunities. In return, you'll benefit from a supportive team, a rich culture, shared success, and the flexibility to work from the comfort of your home.

Tiger Analytics is on the lookout for skilled Machine Learning Engineers to become a part of our rapidly expanding advanced analytics consulting firm. Our team members possess profound knowledge in Machine Learning, Data Science, and Artificial Intelligence. We take pride in being a trusted analytics partner for numerous Fortune 500 companies, helping them extract business value from data. Our leadership and business impact have been acknowledged by renowned market research firms such as Forrester and Gartner.As we strive to assemble the finest global analytics consulting team, we seek exceptional talent to join us. In this role, you will take charge of:Acting as an ML Engineer with 5-7 years of IT experience.Creating and managing pipeline training models, including building, deploying, testing, and monitoring using AWS SageMaker, AWS CloudFormation, AWS CodePipeline, and Lambda.Designing Airflow DAGs to facilitate training and scoring pipelines.Establishing a robust testing framework using Pytest.Implementing a monitoring solution using a custom approach with Lambda and Dash.Developing Data Quality solutions, potentially utilizing Great Expectations.

About the RoleAs a key member of the Galileo team, you will significantly contribute to the design, development, and expansion of our innovative products. We are in search of a talented Senior Software Engineer with a strong interest in tackling intricate challenges at the interface of Data and Machine Learning, and a deep passion for enhancing Observability and Reliability in Generative AI.Your ResponsibilitiesTechnical Design and Architecture: Lead the effort in establishing scalable and dependable architectures while securing stakeholder alignment.Planning and Execution: Collaborate with your team to outline and implement the project roadmap.Peer Reviews: Ensure engineering excellence by conducting thorough reviews of your colleagues' pull requests.Team Collaboration: Work closely with Product Managers, designers, and technical leads to build a cohesive strategy and maximize collaborative efforts.Continuous Improvement: Engage in design reviews, on-call duties, support tasks, and contribute to tech discussions and learning sessions. Assist in the interview process for prospective engineering candidates.

Join our dynamic team at Anthem Engineering, where we are at the forefront of prototyping and developing innovative technologies and solutions to address critical issues within our division. Collaborating closely with analysts, we identify workflow challenges and create effective, user-friendly solutions.Our work encompasses a diverse range of applications and services tailored to support various missions, with an environment that is fast-paced and constantly evolving. Over the past year, we have successfully built tools that: Ingest, process, and analyze extensive data sets for high-stakes Presidential initiatives,Directly support forces safeguarding American lives in high-risk situations,Organize, process, and visualize crucial intelligence data, effectively preventing loss of life,Utilize geospatial tools and analytics to identify and track essential assets, andImplement AI/ML algorithms to tackle real-world challenges. Our strong partnership with government clients is integral to our success. They rely on our insights and recommendations, granting us the autonomy to select the best tools for each unique challenge.Each project we undertake presents distinct operational and technological constraints. We leverage a variety of frameworks, libraries, and programming languages to customize our solutions, including: TypeScript and JavaScript,React, Angular, Material UI, Bootstrap, Storybook,Java, Spring Boot,Elasticsearch, MongoDB, MySQL,npm, Webpack, Maven,Jest, JUnit, andGit. We are committed to continuous learning and the adoption of new tools, so adaptability is key. While we don’t expect everyone to know everything, we value a team that is eager to learn and grow together. Onsite work at customer location is required.

About Phare & R1At Phare, we are revolutionizing the healthcare industry with our groundbreaking Revenue Operating System. Our innovative platform leverages AI technology to simplify hospital billing and reimbursement, delivering accuracy and fairness. As part of R1, a leading healthcare claims management company serving hundreds of systems nationwide, we blend the agility of a startup with the resources of an established healthcare organization. Join us as we strive to create a more equitable and efficient model for healthcare payments.The RoleAs a Software Engineer focused on MLOps, you will be responsible for overseeing the production runtime of Phare’s machine learning stack. Your key tasks will include deploying, serving, and scaling models across various inference endpoints and managing batch/streaming workflows. You will create robust delivery pipelines with automated rollouts and rollbacks, ensure service level objectives for latency and availability, and implement comprehensive observability solutions. You will utilize Terraform, Kubernetes, and CI/CD to strengthen our platform and guarantee reproducible, auditable ML releases.We are looking for candidates at various seniority levels, from mid-level to staff positions. A minimum of 5 years of software engineering experience, including at least 2 years in MLOps, is required.This position requires in-person attendance in our SoHo office at least 3 days a week.About YouYou possess a solid background in managing ML systems at scale, where both uptime and efficient feedback loops are crucial alongside accuracy. Your experience includes:Production ML: Proven expertise in deploying and operating models on GPUs in production environments, including APIs and batch/streaming inference.Platform Engineering: Strong proficiency in Docker/Kubernetes, Infrastructure as Code (e.g., Terraform), and CI/CD processes for services and model artifacts, ensuring environment consistency, reproducible releases, and robust model/versioning with data lineage.System Reliability: Experience in implementing progressive delivery with automated rollouts/rollbacks, and establishing end-to-end observability (metrics, logs, traces, and model telemetry for drift and regression), coupled with actionable alerting, runbooks, and incident response protocols.Post-Training Lifecycles: Competence in managing model registries, stage gates, and designing scheduled or event-driven retraining processes.