01Overview
Your responsibilities:
Responsible for building and leading the Hyderabad engineering unit, including team management, talent development, and ensuring high-quality delivery aligned with global objectives.
Own and refine the sprint-level product backlog for the Hyderabad engineering team, decomposing strategic epics into technically detailed user stories, tasks, and Gherkin acceptance criteria that are immediately actionable by AI/ML and backend engineering squads.
Align sprint planning and delivery cadence with leadershipteam, ensuring seamless cross-timezone coordination and consistent strategic direction.
Collaborate directly with Senior AI Engineers and Senior Backend Developers to evaluate the technical feasibility of new agent capabilities, multi-agent orchestration patterns (LangGraph, agentic RAG, MCP integration), and backend services (identity management, API design, microservice decomposition), ensuring stories are properly scoped and technically sound.
Define and validate quality gates for prompt chains, agent confidence thresholds,and output fidelity benchmarks for foundation models, and coordinate with DevSecOps and platform teams to ensure backend services, identity flows (OAuth2.0/OIDC), Kubernetes deployments on GCP, and CI/CD pipelines meet enterprise-grade reliability and security standards.
Facilitate stakeholder communication across time zones, bridging product vision, engineering capacity, and business priorities between various teams.
Translate EU AI Act, DORA, and NIS2 regulatory requirements into concrete technical acceptance criteria, ensuring every agent action is versioned, logged,auditable, and compliant before release into production.
Drive sprint planning, daily stand-ups, backlog refinement, and retrospectives with a technically informed perspective; remove impediments, clarify technicalambiguities in real time, and synchronise technical dependencies across AgentChain and Agent Hub for the engineering team.
Define, instrument, and monitor technical product metrics, including agent accuracy, latency, API throughput, error rates, automation yield, and deployment frequency, to inform sprint-level prioritisation and continuous improvement.
Your profile:
Bachelor's or Master's degree in Computer Science, Artificial Intelligence,Software Engineering, or a related technical field, with 5+ years of experience in atechnically oriented Product Owner, Technical Product Manager, or Engineering Lead role for AI/ML or platform products.
Strong hands-on technical background in AI/ML systems, including practical experience with or deep understanding of LLM-based architectures, multi-agent orchestration frameworks (LangGraph, LangChain), prompt engineering, and Retrieval-Augmented Generation (RAG) patterns.
Solid understanding of enterprise backend engineering, including microservice architecture, RESTful and event-driven API design, OAuth 2.0/OIDC identity flows,and distributed systems patterns.
Proficiency in reading, reviewing, and reasoning about Python and/or Java codebases; ability to participate in architecture discussions and technical design reviews as a credible engineering partner.
Strong agile backlog management skills with proven experience in Kubernetes,GCP, CI/CD pipelines (GitHub Actions, Terraform), and the ability to decompose complex technical initiatives into well-defined, estimable, and testable increments.
Nice-to-have: Experience in regulated industries (financial market infrastructure, EU AI Act, DORA, NIS2), hands-on experience with vector databases and context- grounding techniques (MCP), certified CSPO or Protected
