Lead Advanced Analytics Engineer (AI)

Job Description Roles & Responsibilities Build and deploy production-grade Generative AI and Agentic AI solutions across the organization. Develop multi-agent systems and agent-to-agent (A2A) orchestration for autonomous, end-to-end workflows. Build RAG pipelines with vector databases and embeddings to ground LLMs in enterprise data. Design, train, and deploy Deep Learning models (NLP, CV, time-series) using PyTorch. Use AWS (Bedrock, SageMaker) and Azure (OpenAI, ML Studio) to host and scale AI workloads. Integrate Snowflake Cortex AI/ML, SAP Joule, and MuleSoft AI Chain with enterprise systems. Apply prompt engineering, function calling, and guardrails for reliable LLM apps. Operationalize models with MLOps/LLMOps: CI/CD, evaluation, monitoring, and responsible AI. Key Responsibilities: Build LLM apps and agentic workflows using LangChain, LangGraph, LlamaIndex, AutoGen, or CrewAI. Design agent-to-agent (A2A) orchestration: planning, tool use, memory, and multi-agent collaboration. Develop RAG pipelines: chunking, embeddings, vector search, re-ranking, and grounding. Train and fine-tune deep learning models with PyTorch. Deploy models on AWS Bedrock, SageMaker, Azure OpenAI, and Azure ML Studio. Integrate AI with Snowflake Cortex, SAP Joule, and MuleSoft AI Chain. Apply prompt engineering, evaluation, and guardrails for safe, accurate outputs. Implement MLOps/LLMOps: experiment tracking, CI/CD, monitoring, and drift detection. Collaborate with engineering, data, and business teams and document architectures. Deliverables: Production GenAI and agentic applications adopted by business users. Working multi-agent systems with A2A orchestration automating key workflows. Reliable RAG pipelines with accurate, grounded LLM responses. Trained and deployed deep learning models meeting accuracy, latency, and cost targets. Successful integrations with Snowflake Cortex, SAP Joule, and MuleSoft AI Chain. End-to-end MLOps/LLMOps pipelines for training, deployment, and monitoring. Responsible AI guardrails ensuring quality, safety, and governance. Reusable AI components, prompt libraries, and reference architectures. Clear technical documentation and architecture diagrams. Measurable business outcomes: efficiency, automation, and better user experience. Desired Candidate Profile 5+years of experience Bachelor's degree in Computer Science, AI, Data Science, Software Engineering, or Mathematics. Certifications in AWS, Azure, Snowflake, GenAI, or ML preferred. Equivalent hands-on AI/ML experience may substitute for certifications. Mandatory Technical Skills: Strong Python with NumPy, Pandas, scikit-learn, PyTorch, TensorFlow, Hugging Face. Hands-on with LLM and agent frameworks: LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI. Experience designing agent-to-agent (A2A) orchestration: tool use, planning, function calling, memory. Practical RAG experience with vector DBs (Pinecone, Weaviate, Chroma, FAISS, pgvector). Solid Deep Learning: transformers, CNNs, RNNs, fine-tuning, and model evaluation. Hands-on with AWS AI/ML: Bedrock, SageMaker, Lambda, S3. Hands-on with Azure AI/ML: Azure OpenAI, ML Studio, AI Foundry. Good experience with Snowflake AI/ML: Cortex AI and Snowpark ML. Good experience with SAP Joule: GenAI copilot and SAP integrations. Good experience with MuleSoft AI Chain on Anypoint Platform. Proficient in prompt engineering, evaluation, and guardrails. Experience with REST APIs, Git, Docker, and CI/CD for models. Solid MLOps/LLMOps: tracking, versioning, monitoring, responsible AI. Strong analytical, problem-solving, and communication skills. Tools and Technologies: Languages & Frameworks: Python, PyTorch, TensorFlow, Hugging Face, scikit-learn, FastAPI. LLM & Agent Frameworks: LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, Semantic Kernel. LLM Providers: OpenAI, Anthropic Claude, Llama, Mistral, Cohere, Hugging Face. Cloud AI: AWS Bedrock, SageMaker, Azure OpenAI, Azure ML Studio, AI Foundry. Enterprise AI: Snowflake Cortex & Snowpark ML, SAP Joule, MuleSoft AI Chain. Vector DBs: Pinecone, Weaviate, Chroma, FAISS, pgvector, Milvus. Data & Storage: Snowflake, SQL, PostgreSQL, MongoDB, S3, Azure Data Lake, Pandas, Spark. MLOps/LLMOps: MLflow, Weights & Biases, LangSmith, LangFuse, Docker, Kubernetes, Git CI/CD. Evaluation: RAGAS, DeepEval, TruLens, Arize. Collaboration: Jupyter, VS Code, Jira, Confluence, Postman. Company Industry IT - Software Services Department / Functional Area Engineering Keywords Lead Advanced Analytics Engineer (AI) Get real-time job updates only on our App