Lead AI Architect - Machine Learning & Generative AI

Role Summary:

We are looking for a hands-on Lead AI Architect to design, build, and deploy scalable Machine Learning and Generative AI solutions in production. You will drive AI strategy and architecture, partner with business stakeholders, and ensure robust, governed, and responsible AI adoption across the organization.

Key Responsibilities:

- Design and own end-to-end AI/ML solution architecture for strategic, group-wide use cases with scalability, real-time inference, and governance.

- Develop, train, and validate ML/GenAI models using deep learning, ensemble methods, LLMs, agentic systems, and rigorous A/B testing.

- Build production-grade AI/ML pipelines: training, evaluation, deployment, monitoring, retraining; implement MLOps/AIOps, versioning, and reproducibility.

- Implement AI guardrails, safety, and compliance: fairness, explainability, regulatory requirements, and risk controls for AI systems.

- Drive responsible AI adoption with clear metrics, benchmarks, and governance processes.

- Translate business problems into AI/ML opportunities, support value streams in building their own use cases, and align with group AI strategy.

- Set up model monitoring & governance for performance drift, data drift, business metrics, and model lifecycle (versioning, approval, retirement).

- Engage stakeholders across business, tech, and compliance; communicate updates, gather feedback, and drive adoption.

- Maintain clear documentation (technical specs, user guides, project plans) and regular status reports on AI product development and deployment.

- Stay updated on AI/ML trends, and help implement/improve MLOps frameworks and best practices across teams.

Minimum Experience & Key Competencies:

- 10+ years in end-to-end AI/ML engineering, AI architecture, or related roles.

- Proven track record of delivering 3+ production ML/AI solutions, preferably in enterprise or financial services.

- Hands-on experience deploying GenAI/LLM solutions integrated with applications using OpenAI GPT, Google Gemini, Mistral, etc., and orchestration frameworks like LangChain, LangGraph.

- Strong Python skills with clean, modular, testable code; experience with design patterns and building APIs using FastAPI in enterprise settings.

- Hands-on with public cloud (AWS/GCP/Azure); multi-cloud experience is a plus.

- Experience deploying AI at scale with big data, low-latency inference, feature serving, and streaming data for ML.

Functional knowledge of:

- LLMs: prompt engineering, fine-tuning, RAG, agentic systems, production considerations.

- Model lifecycle: version control, reproducibility, approval workflows, monitoring dashboards, retirement.

- Vector databases, embeddings, semantic search: for RAG and agentic AI.

- Model security: adversarial risks, privacy, encryption, and compliance with data-privacy regulations.

- Bias detection, fairness metrics, explainable AI: and regulatory requirements around algorithmic transparency.

- Ability to translate business needs into AI solutions, understand ROI and business metrics, and drive adoption/commercialization.

- Highly collaborative, outcome-oriented, and comfortable navigating complex, cross-functional challenges.