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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
