Description

Key Responsibilities

• Partner with an assigned technology organization as the primary GenAI engineering lead, translating business priorities into scalable AI solutions
• Design, build, test, and deploy production-grade GenAI solutions using LLMs, RAG, and agentic AI patterns
• Speed up high-value use cases through rapid prototyping, reusable components, and reference architectures
• Drive adoption of enterprise AI platforms such as Azure AI and AWS Bedrock, turning platform capabilities into practical implementation patterns
• Design secure RAG and agentic workflows with document ingestion, embeddings, vector search, orchestration frameworks, tools/APIs, and guardrails
• Make sure solutions meet security, privacy, responsible AI, governance, observability, and production support standards
• Run workshops, training, and design reviews on prompt engineering, RAG, orchestration, and LLM application patterns
• Create playbooks, quick-start guides, sample code, and best practices, and act as the link between the central AI platform team and engineering teams

Required Skills

• 8+ years in software engineering, data engineering, ML engineering, or AI solution development in enterprise environments
• 5+ years building ML, AI, or data-driven applications in Python
• 3+ years building GenAI/LLM applications, including prompt engineering, model integration, evaluation, and production deployment
• 3+ years designing RAG solutions with embeddings, vector search/databases, document processing, and grounding patterns
• 2+ years building agentic AI or multi-step AI workflows with orchestration frameworks, tool/function calling, and enterprise integrations
• 3+ years with cloud AI/ML platforms (Azure AI / Azure OpenAI, AWS Bedrock, or comparable)
• 3+ years in cloud-native engineering: CI/CD, containers, observability, security controls, and production deployment
• 2+ years in technical enablement, architecture, consulting, or engineering leadership (workshops, mentoring, responsible AI/governance)
• Bachelor’s degree in Computer Science, Engineering, Data Science, AI, ML, or equivalent experience

Preferred Skills

• LangChain, LangGraph, Semantic Kernel, LlamaIndex, AutoGen, or similar orchestration frameworks
• Vector technologies such as Azure AI Search, OpenSearch, Pinecone, pgvector, or FAISS
• LLMOps/MLOps, prompt and model evaluation, AI observability, guardrails, and red-teaming
• Reusable GenAI accelerators or AI delivery in regulated environments such as financial services

Key Skills

Education

Bachelor's degree