Responsibilities
- Identify and prioritize GenAI use cases across departments through stakeholder partnerships and operational deep-dives.
- Architect and develop prototype agents using private ChatGPT instances, Azure OpenAI, Anthropic Claude, and open-source LLMs
- Implement RAG systems, multi-agent orchestration, and intelligent automation using frameworks such as LangChain, LlamaIndex, or LangGraph.
- Build and deploy automated PoCs that demonstrate feasibility, value, and integration potential with enterprise systems.
- Evaluate and tune prompt engineering strategies, tool integrations, and memory handling for agent reliability and accuracy.
- Build API services (FastAPI) integrating with enterprise systems (AS400/IBM i, Salesforce, Oracle, etc.) and Snowflake dataStay on top of GenAI research, LLM fine-tuning techniques, and agentic design patterns to continually evolve our internal capabilities.
- Collaborate with Data Engineers and Software Engineers to transition PoCs into robust, secure, and scalable enterprise applications.Create documentation, reusable components, and establish GenAI engineering standards to accelerate AI adoption across the company.
- Deploy applications on Azure infrastructure with CI/CD pipelines, MLOps workflows, monitoring, and cost optimization
Basic qualifications
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
- 4+ years of experience in AI/ML development, with 2+ years building GenAI applications deployed to production.
- Proven experience developing LLM agents using frameworks such as LangChain, AutoGen, or similar orchestration layers with measurable business value.
- Strong Python development skills, including experience with FastAPI or similar frameworks.
- Familiarity with tools for secure enterprise deployment (e.g., Azure OpenAI, private GPT instances, vector databases, RAG pipelines).
- Hands-on experience building autonomous agents or copilots for enterprise use cases (e.g., workflow automation, content generation, monitoring).
- Experience designing PoCs with measurable business outcomes and communicating value to both technical and non-technical audiences.
- Strong collaboration, communication, and project management skills in a cross-functional environment.
Preferred qualifications
- Experience working within or supporting large industrial or B2B distribution businesses.
- Familiarity with DevOps or MLOps workflows for AI model deployment.
- Exposure to data privacy, governance, and secure model deployment in regulated enterprise environments.
- Prior work with open-source or commercial RAG systems, embedding models, or vector search (e.g., FAISS, Weaviate, Pinecone).
- Ability to mentor other developers or analysts on GenAI development best practices.
Tags & focus areas
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