and data-intensive technical initiatives across multiple products.
Define and evolve data management strategy, including data modeling standards, pipeline requirements, governance, lineage, and quality frameworks.
Lead product strategy for agentic systems, including orchestration layers, tool usage patterns, memory/context management, guardrails, and fallback strategies.
Establish and operationalize evaluation frameworks for AI products (LLM evals, benchmarking, human-in-the-loop review, automated scoring, drift monitoring).
Partner with engineering to design scalable architectures that support data ingestion, transformation, context retrieval, and multi-agent coordination.
Translate business objectives into clear technical specifications spanning APIs, data contracts, orchestration logic, and observability requirements.
Define metrics for success across system performance, data quality, model reliability, latency, cost optimization, and user outcomes.
Identify and mitigate risks across model behavior, data integrity, security, and compliance.
Oversee validation processes for AI systems, including regression testing, prompt versioning, evaluation harnesses, and continuous improvement loops.
Act as a bridge between product, data engineering, ML engineering, and platform teams to ensure technical alignment and delivery excellence.
Communicate roadmap progress, architectural trade-offs, and performance insights to executive stakeholders with clarity and rigor.
Foster a culture of accountability, structured experimentation, and high technical standards across teams.
Requirements
Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related technical field.
2–5 years of experience in technical product management, preferably in AI, ML, or data platform environments.
Strong understanding of data architecture and data management principles, including data modeling, governance, lineage, quality monitoring, and cloud data platforms.
Demonstrated experience working on AI-powered or agent-based products, including orchestration patterns and evaluation methodologies.
Familiarity with LLM concepts such as context windows, retrieval augmentation (RAG), prompt orchestration, tool calling, memory management, and guardrails.
Deep understanding of cloud computing platforms, distributed systems, APIs, and integration patterns.
Hands-on exposure to Agile methodologies, DevOps practices, and CI/CD pipelines.
Strong analytical thinking, structured problem-solving, and data-driven decision-making skills.
Excellent communication and stakeholder management abilities, including executive-level reporting.
Tech Stack
Cloud
Distributed Systems
Benefits
Comprehensive Benefits: We cover 100% of health, dental, and vision insurance premiums for you and your dependents which means no out-of-pocket costs. Eligibility starts from day one itself.
Growth & Learning: Access extensive learning and development resources to keep leveling up your skills.