About the job
Join us at Riverflex to create innovative AI systems that drive real business results!
At Riverflex, we don’t just discuss AI—we implement it. As an AI Engineer, you will be an integral part of a close-knit, high-impact team dedicated to developing intelligent solutions that merge cutting-edge software engineering with advanced language models and machine learning techniques. Your contributions will help design and deploy scalable AI systems that enhance next-generation digital products for our clients as well as internal tools.
We seek a candidate who possesses an in-depth understanding of large language models (LLMs), fundamental machine learning principles, and AI engineering. You should be adept at transforming theoretical concepts into functional code and excel at the confluence of product development, data analytics, and engineering. If you have experience leading AI project delivery, developing Generative AI applications, and scaling solutions with a focus on quality, this role is tailored for you.
Your Role:
In this hands-on lead engineer position, you will architect and construct AI-powered services utilizing LLMs, contemporary orchestration frameworks, and robust engineering methodologies. You will collaborate closely with data, product, and software teams to integrate these systems into actionable, real-world applications. A key aspect of your role will involve enhancing our AI capabilities, creating frameworks, accelerators, best practices, and mentoring fellow AI engineers.
Key Responsibilities:
- Develop scalable AI and Generative AI systems using transformer-based models (e.g., GPT, Mistral, Claude) and Retrieval-Augmented Generation (RAG) architectures.
- Design and implement ML/AI pipelines encompassing model training, evaluation, prompt chaining, embedding retrieval, and context management (MCP protocols).
- Craft modular, well-tested Python code for AI agents, APIs, and microservices.
- Adopt ML Ops practices to ensure reproducible training, deployment, and monitoring of models in production environments.
- Utilize orchestration tools (LangChain, Semantic Kernel, n8n) to implement agent workflows and deliver end-to-end AI experiences.

