Job Summary

• 15+ years in Data/AI/ML Engineering or 15+ years with Master’s
• Deep experience in Azure Data & AI stack + working knowledge of GCP & AWS
• Hands-on with:
  • ML projects (supervised/unsupervised, forecasting, NLP, deep learning)
  • ML modeling tools: Python, PySpark, Azure ML, Databricks, Scikit-learn
  • LangChain, LangGraph, Agentic AI systems
  • LLMs, embeddings, vector databases, RAG/GraphRAG
  • CI/CD, MLOps/LLMOps, SDLC
  • Explainable AI (XAI)
• Strong exposure to Synapse, Fabric, Data Factory, Databricks, Power BI
• Proven experience contributing to RFP/RFI responses and client solutioning

Key Responsibilities

Responsibilities

• Architect Agentic AI solutions using Azure OpenAI, LangChain, LangGraph & multi-agent frameworks
• Build RAG pipelines, vector DB integrations & autonomous workflow orchestration
• Design and lead ML project lifecycles — data prep, modeling, training, evaluation, deployment & MLOps
• Govern full SDLC for Data, ML, and GenAI platforms
• Implement XAI/Explainable AI (SHAP, LIME, fairness, transparency models)
• Architect and deploy solutions across Azure, GCP, AWS
• Drive RFP/RFI solutioning, technical proposals, estimations & client workshops
• Ensure strong security, compliance, governance (GDPR, CCPA, PII)
• Produce robust architecture blueprints, ML design docs, and runbooks
• Engage with customer IT and business leaders to understand pain points, priorities, success measures, and risks. 
• Design secure, scalable data and AI solutions to deliver measurable business value.
• Lead architecture design sessions, develop data/AI and analytics roadmaps to drive PoCs and MVPs. 
• Accelerate adoption and ensure long-term technical viability.
• Maintain deep understanding of evolving AI capabilities, competitive insights, and market trends.
• Demonstrate a growth mindset by continuously aligning your skills to team and customer needs,
• Contribute to technical communities and mentor others to accelerate customer outcomes.

Skill Requirements

• 15+ years in Data/AI/ML Engineering or 15+ years with Master’s
• Deep experience in Azure Data & AI stack + working knowledge of GCP & AWS
• Hands-on with:
  • ML projects (supervised/unsupervised, forecasting, NLP, deep learning)
  • ML modeling tools: Python, PySpark, Azure ML, Databricks, Scikit-learn
  • LangChain, LangGraph, Agentic AI systems
  • LLMs, embeddings, vector databases, RAG/GraphRAG
  • CI/CD, MLOps/LLMOps, SDLC
  • Explainable AI (XAI)
• Strong exposure to Synapse, Fabric, Data Factory, Databricks, Power BI
• Proven experience contributing to RFP/RFI responses and client solutioning

Other Requirements

• 15+ years in Data/AI/ML Engineering or 15+ years with Master’s
• Deep experience in Azure Data & AI stack + working knowledge of GCP & AWS
• Hands-on with:
  • ML projects (supervised/unsupervised, forecasting, NLP, deep learning)
  • ML modeling tools: Python, PySpark, Azure ML, Databricks, Scikit-learn
  • LangChain, LangGraph, Agentic AI systems
  • LLMs, embeddings, vector databases, RAG/GraphRAG
  • CI/CD, MLOps/LLMOps, SDLC
  • Explainable AI (XAI)
• Strong exposure to Synapse, Fabric, Data Factory, Databricks, Power BI
• Proven experience contributing to RFP/RFI responses and client solutioning