Enterprise Architect

HCLTech; India; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

HCL Technologies Limited is seeking an accomplished AI/ML leader with 20+ years in IT and 15+ years designing and delivering enterprise AI/ML solutions. The role focuses on AI architecture, end-to-end delivery of multiple programs, and responsible AI governance in client-facing, solution-architecture contexts.

You will lead cross-functional teams, shape large engagements, and mentor data scientists and ML engineers while defining guardrails for GenAI usage and scalability. Masters/PhD preferred.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics or a related field.
• A Master's or PhD is preferred.

Responsibilities

• AI architecture and strategy: Define the enterprise AI reference architecture and roadmap across ML, deep learning, GenAI, RAG and agentic systems.
• Delivery ownership: Lead end-to-end delivery of multiple concurrent AI programs, from use-case discovery and proof of concept to production and scale.
• Solution design and review: Architect scalable, secure and cost-efficient solutions. Review designs for data pipelines, model serving, latency and inference cost.
• MLOps and LLMOps: Set standards for model CI/CD, experiment tracking, feature stores, evaluation, monitoring, drift detection and rollback.
• Responsible AI and governance: Build security, privacy, fairness, explainability and regulatory compliance into every solution.
• Stakeholder engagement: Work with CXOs, business owners and clients to turn business problems into AI solutions with measurable ROI.
• Team leadership: Lead and mentor data scientists, ML engineers and solution architects.
• Innovation and reuse: Track new research and tools, run proofs of concept, and build reusable accelerators and IP.

Skills

AI architecture

Delivery ownership

Solution design

MLOps/LLMOps

Responsible AI

Stakeholder engagement

Team leadership

Innovation

Education

Bachelor's degree

Master's or PhD preferred

Job description

20+ years in IT, with at least 15+ years designing and delivering AI/ML solutions at enterprise scale.A proven record of leading multiple AI programs in parallel to production, with cross-functional teams, budgets and measurable business outcomes.Hands-on depth across classical ML, deep learning (NLP, computer vision, time series) and GenAI (LLMs, RAG, fine-tuning, agents).Production experience on at least one major cloud (AWS, Azure or GCP) and its AI/ML services.Experience in client-facing, consulting or solution-architect roles, including shaping large engagements and proposals.Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics or a related field. A Master's or PhD is preferred.

Key Responsibilities

• Key responsibilities
• AI architecture and strategy: Define the enterprise AI reference architecture and roadmap across ML, deep learning, GenAI, RAG and agentic systems. Make build, buy or partner calls on models, platforms and vendors.
• Delivery ownership: Lead end-to-end delivery of multiple concurrent AI programs, from use-case discovery and proof of concept to production and scale. Own scope, timelines, quality and risk.
• Solution design and review: Architect scalable, secure and cost-efficient solutions. Review designs for data pipelines, model serving, latency and inference cost.
• MLOps and LLMOps: Set standards for model CI/CD, experiment tracking, feature stores, evaluation, monitoring, drift detection and rollback.
• Responsible AI and governance: Build security, privacy, fairness, explainability and regulatory compliance into every solution. Define guardrails for GenAI use.
• Stakeholder engagement: Work with CXOs, business owners and clients to turn business problems into AI solutions with measurable ROI. Support solution shaping, pre-sales and executive presentations.
• Team leadership: Lead and mentor data scientists, ML engineers and solution architects. Run the architecture review board and hire for the team.
• Innovation and reuse: Track new research and tools, run proofs of concept, and build reusable accelerators and IP.