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 cloud?native 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
Vendor submissions - 12 months
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