Key Responsibilities

  • Define and drive enterprise-level architecture for Data Science, AI/ML, Generative AI, and Agentic AI solutions, ensuring alignment with organizational strategy and technology roadmaps
  • Lead the design of scalable, secure, and high-performance AI platforms, covering data, model, orchestration, and serving layers across multiple business domains
  • Establish architectural standards, design patterns, and reusable frameworks for Machine Learning, Deep Learning, Generative AI, and Agentic AI systems
  • Own and govern the end-to-end AI/ML ecosystem, including data pipelines, feature stores, model training environments, inference layers, and monitoring systems
  • Define and institutionalize best practices for MLOps and LLMOps, at scale, including multi-environment deployments, governance, observability, cost optimization, and lifecycle management
  • Architect and oversee enterprise-grade Generative AI and Agentic AI platforms, including RAG architectures, multi-agent orchestration, tool integration, memory management, and guardrails
  • Provide architectural oversight and technical direction across multiple teams, ensuring consistency, scalability, and reusability of AI solutions
  • Collaborate with senior stakeholders (Product, Engineering, Data, Security, Governance) to translate business strategy into AI driven solution blueprints
  • Lead technology evaluations, define platform strategies, and guide adoption of emerging tools, frameworks, and AI capabilities
  • Ensure compliance with AI governance frameworks, including security, privacy, ethical AI, and regulatory standards
  • Mentor Tech Leads and senior engineers, driving architectural maturity and capability building across the organization
  • Act as a key contributor in Architecture Review Boards (ARB) and strategic decision-making forums

Person Specifications

  • Bachelor's degree in IT, Computer Science, Software Engineering, Data Science, Engineering, Mathematics, or a related field
  • 8–10 years of professional experience in Data Science, AI, or ML, working in production-grade environments, with significant experience in solution architecture and enterprise-scale system design

Technical Expertise

  • Deep expertise in Machine Learning and Deep Learning, including advanced model design, optimization, and large-scale deployment
  • Extensive hands-on and architectural experience in Generative AI (LLMs, RAG pipelines, embeddings, fine-tuning, evaluation frameworks)
  • Strong experience designing Agentic AI systems (multi-agent architectures, orchestration frameworks, tool ecosystems, autonomous decision-making)
  • Proven track record in implementing MLOps practices at scale (CI/CD for ML, automated pipelines, monitoring, retraining strategies)
  • Advanced expertise in LLMOps, including prompt lifecycle management, evaluation pipelines, guardrails, observability, latency, and cost optimization
  • Strong programming skills in Python and deep familiarity with AI/ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn)
  • Experience with cloud platforms (AWS, Azure, or GCP) and cloudnative platforms & services (e.g., Copilot Studio, Bedrock, Vertex AI, Azure OpenAI)
  • Strong understanding of data engineering and data platform architecture (ETL/ELT pipelines, feature stores, data lakes/warehouses)
  • Experience with distributed systems, microservices, APIs, and event driven architectures in AI contexts

Leadership and Architectural Skills

  • Strong system thinking and ability to design end-to-end enterprise AI architectures
  • Proven leadership in guiding multiple teams and influencing senior stakeholders
  • Experience defining and enforcing architecture governance, standards, and best practices
  • Excellent communication and stakeholder management skills, including C-level engagement
  • Strong focus on scalability, reliability, security, and cost-efficiency in AI systems
  • Ability to balance innovation with practical, production-grade delivery