Technical Product Manager

Role Overview

• Own and drive the end-to-end lifecycle of AI
• 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.
• Experience defining and operationalizing evaluation frameworks (offline/online testing, experimentation, quality metrics, performance benchmarking).
• 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.