Technical Architect
Hyderabad · Executive
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Job description
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
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