• Define and own the enterprise AI, ML strategy, building a multi-year roadmap aligned to digital and business transformation priorities

• Lead and scale a high-performing team of AI Engineers, MLOps Engineers, Data Engineers, Application Engineers and Data Scientists

• Architect and oversee development of enterprise-grade AI solutions including LLM-based systems, Retrieval-Augmented Generation (RAG), predictive ML models, decision intelligence platforms, and data-heavy applications

• Drive the full AI product lifecycle - opportunity discovery, business case definition, architecture design, development, deployment, monitoring, optimization and value realization

• Establish enterprise AI reference architectures, reusable frameworks, modular ML templates and standardized deployment patterns

• Ensure strong MLOps maturity including CI/CD for ML, model registry, automated retraining pipelines, monitoring for data/model drift, observability and governance

• Lead cloud-native AI platform strategy leveraging Azure / Databricks / AWS / GCP ecosystems with focus on scalability, resiliency and cost efficiency

• Define GenAI standards including prompt engineering frameworks, evaluation pipelines, guardrails, responsible AI controls and security compliance

• Partner with business leaders to translate complex operational challenges into scalable AI and data products with measurable ROI

• Define and track product KPIs including adoption, performance, latency, uptime, cost per inference, business value creation and productivity uplift

• Ensure enterprise-grade security, compliance, and responsible AI practices across all deployed solutions

• Drive continuous innovation by evaluating emerging AI technologies and integrating them into the enterprise ecosystem where strategically relevant

• Act as the single point of accountability for AI & Data product performance, delivery excellence and long-term platform scalability

• University degree (B.Tech / M.Tech / M.S.) in Computer Science, Engineering or related discipline; MBA or equivalent business exposure is a plus

• 12+ years of experience across AI/ML, Data Platforms, or Enterprise Product Engineering, with 5+ years leading cross-functional technical teams

• Proven track record of building and scaling enterprise AI/ML products in production environments

• Deep understanding of:

• Machine Learning lifecycle and model governance

• LLMs, transformer architectures, RAG pipelines and prompt engineering strategies

• MLOps frameworks including model monitoring, drift detection, retraining and CI/CD for ML

• Modern data architectures (Databricks, Spark, ETL/ELT, Delta patterns, data orchestration)

• API-driven architectures and distributed systems design

• Strong exposure to cloud ecosystems (Azure, Databricks, AWS or GCP) and cost optimization strategies for large-scale AI workloads

• Experience designing scalable, secure and highly available enterprise data platforms

• Strong strategic thinking with ability to align AI investments to business value, operational efficiency and competitive advantage

• Demonstrated experience influencing senior stakeholders and translating technical trade-offs into business decisions

• Strong leadership and talent development capability, with experience building engineering culture and technical maturity

• Excellent communication skills with ability to operate across business and technical audiences

• Hands-on mindset with ability to dive deep into architecture discussions when required

• Ability to operate in a fast-paced, high-growth and transformation-driven environment

• Fluent in Business English