Technical Architect

Job description

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 Apply now Information at a Glance Why HCLTech? At HCLTech, you'll supercharge your potential. You'll find your career. And you'll find your spark. All at a place that knows that helping its customers stay on top starts by putting its people first. HCLTech is a global technology company, home to more than 223,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud and AI, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of 12 months ending June 2026 totaled $14.8 billion. Disclaimer Privacy Statement Terms of use Sitemap Raise a Grievance We use cookies on our site. Please read more about cookies policy here. Opens in a new tab. Opens in a new tab. Opens in a new tab. Opens in a new tab. Opens in a new tab. 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