AI Engineer

Your future duties and responsibilities
Lead the design and development of scalable Generative AI solutions using LLMs
Define and implement architectures for RAG, agentic, multi-agent, and multimodal systems
Review and guide solution designs to ensure alignment with AI CoE standards and enterprise architecture
Mentor and guide developers on GenAI patterns, tools, and best practices
Develop end-to-end AI solutions using Python
Design, test, and optimize prompt engineering strategies
Build and manage embeddings, vector search, and semantic retrieval pipelines
Develop applications using LangChain and monitor, evaluate, and debug workflows using LangSmith
Integrate LLMs with enterprise systems, APIs, and structured/unstructured data sources
Implement agent-based and multi-agent workflows for task orchestration and automation
Work with multimodal models involving text, documents, and images
Work closely with the AI CoE to align on AI frameworks, reusable components, and architectural standards
Contribute to enterprise GenAI accelerators, reference architectures, and best practices
Support governance, security, responsible AI, and compliance requirements
Share learnings, patterns, and improvements across teams to drive consistency and adoption
Apply deep understanding of LLM capabilities, limitations, and optimization techniques
Implement techniques such as RAG, tool/function calling, memory, and agents
Evaluate and recommend appropriate models (OpenAI, Azure OpenAI, open-source models)
Ensure performance, scalability, cost optimization, and reliability of AI solutions

Must-Have Skills:
Strong programming experience in Python and API development using FastAPI or similar frameworks.
Hands-on experience with OpenAI, Azure OpenAI, Anthropic Claude, and Google Gemini.
Build AI applications using LangChain, LangGraph, OpenAI Agents SDK, and Semantic Kernel.
Design and implement Agentic AI solutions, including multi-agent workflows, tool orchestration, memory management, and human-in-the-loop capabilities.
Develop RAG solutions using embeddings, chunking, vector search, reranking, and enterprise knowledge retrieval.
Experience with Model Context Protocol (MCP), function calling, and enterprise tool integrations.
Hands-on experience with Vector Databases such as Pinecone, ChromaDB, pgvector, FAISS, or Azure AI Search.
Strong knowledge of Prompt Engineering, Context Engineering, prompt optimization, and AI evaluation techniques.
Experience with relational and NoSQL databases (PostgreSQL, MongoDB, Redis) and enterprise data integration.
Strong analytical, problem-solving, debugging, communication, and stakeholder management skills.
Good-to-Have Skills:
Deploy and manage AI applications on AWS, Docker, and OpenShift/Kubernetes using CI/CD pipelines.
Build secure, scalable AI solutions with monitoring, guardrails, observability, and Responsible AI practices.
Collaborate with product, architecture, and engineering teams to deliver AI-powered enterprise solutions

• Agentic AI
• FastAPI
• LangChain
• LangGraph
• Model Context Protocol Client
• Python
• Agentic RAG
• Large Language Model (LLM)
• Retrieval-Augmented Gen.(RAG)

That same commitment to fairness extends to how we use technology. To support our recruitment team, AI tools may be used to help assess applications though they never replace human judgement. All hiring decisions remain entirely in the hands of our recruitment professionals.