Technical Architect

HCLTech • IT Consulting & Digital Transformation • IT Outsourcing & Dedicated Teams • Master's Degree • Python • Power BI • Azure Data Factory • 11 hours ago

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Salary

≈ $155k – $288k per year (Estimated)

Location

In office (United States)

Seniority

Architect · 15+ years exp

Confirmed on the employer's own hiring board on Oct 20, 2026. First seen by Alion on Oct 20, 2026.

Overview

Company

Impact

Profile match

HCLTech

HCLTech is a major Indian multinational information technology (IT) services and consulting company headquartered in Noida, Uttar Pradesh. Spun off from the original HCL Group in 1991, it ranks as one of India's largest technology companies alongside firms like TCS, Infosys, and Wipro.

hcltech.com • HQ: Noida, India • Systems Integrators • IT Consulting & Digital Transformation • IT Outsourcing & Dedicated Teams • 5000+ employees • Est. 1976

Tech stack of HCLTech

Job Summary

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 & Experience

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 & Experience

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

Skill Requirements

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 & Experience

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

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 & Experience

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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