AI Engineer

Build cutting-edge AI systems with real business impact 

At Riverflex, we don’t just talk about AI—we build it. As an AI Engineer, you’ll be part of a small, high-impact team developing intelligent solutions that blend modern software engineering with state-of-the-art language models and machine learning techniques. You’ll help design and deploy scalable AI systems that power next-generation digital products for clients and internal tools. 

We’re looking for someone who deeply understands LLMs, core ML fundamentals, and AI engineering, knows how to turn theory into working code, and thrives at the intersection of product, data, and engineering. If you’ve led AI delivery, built GenAI apps, and know how to scale with quality—this role is for you. 

The Role 

As a hands-on lead engineer, you’ll design and build AI-powered services using LLMs, modern orchestration frameworks, and robust engineering practices. You’ll partner closely with data, product, and software teams to integrate these systems into real-world applications. You’ll also play a key role in growing our AI expertise & capability, developing frameworks/accelerators/best practices/etc. and mentoring our AI engineers. 

Responsibilities 

• Build scalable AI and GenAI systems using transformer-based models (e.g. GPT, Mistral, Claude) and RAG architectures 

• Design and implement ML/AI pipelines including model training, evaluation, prompt chaining, embedding retrieval, and context management (MCP protocols) 

• Engineer modular, well-tested Python code for AI agents, APIs, and microservices 

• Apply ML Ops practices for reproducible training, deployment, and monitoring of models in production 

• Use orchestration tools (LangChain, Semantic Kernel, n8n) to implement agent workflows and end-to-end AI experiences 

• Collaborate with product and engineering teams to integrate AI into user-facing applications 

• Partner with data engineering to build feature stores, vector search capabilities, and serve curated data 

• Optimize AI systems for cost, latency, and scalability across Azure infrastructure (e.g., Azure ML, Azure AI Services) 

• Lead on best practices around prompt evaluation, testing, model performance monitoring, and human-in-the-loop feedback 

• Mentor and guide teammates (internally and at clients) on AI Engineering 

• Champion responsible AI design, including bias mitigation and data privacy safeguards