Technical Program Manager- DevOps-Cloud Platform Engineering(12+Years)

Position: Manager Consulting Delivery- Technical Program Manager- DevOps-Cloud Platform Engineering

Experience: 12+Years

Shift: General Shift (Flexibility required for global stakeholder engagements)

Location: Bangalore/Hyderabad/Pune/Mumbai

Employment Type: Full Time

Education Qualification: Bachelors degree in computer science or related field or higher with minimum 12 years of relevant experience.

Technical Program Manager accountable for delivering the CCOE transformation program: an Operations Control Plane, The role is AI-first by mandate: the TPM is expected to design, prove, and scale AI-assisted operations across the program. It combines hands-on technical credibility someone who still builds, prototypes, and reviews code with large-scale program leadership across engineering, operations, and executive stakeholders, and a demonstrated bias for speed and measurable outcomes.

Your future duties and responsibilities

• Plan and run PI cadences: scope, dependencies, capacity, exit criteria, and transparent re-planning across all CCOE workstreams.
• Drive wave-based rollouts (e.g., observability across multi-tenant AKS estates) within client change windows and government compliance constraints.
• Build funding and business cases spanning multiple internal organizations; force decisions through steering with stated cost of delay.
• Referee build-vs-buy and competing-architecture decisions against written criteria; time-box debates and document decisions with revisit conditions.
• Lead vendor evaluation and enterprise contract negotiation for platform tooling — pricing, support SLAs, security and compliance terms.
• Report program outcomes without spin: MTTR, escalation rate, automation coverage, and SLA adherence.
• Partner with operations leadership on workforce transition: reskilling, redeployment, and adoption of new tooling and workflows — including AI literacy and safe, governed use of AI tooling.
• Own the AI roadmap for operations: LLM-assisted triage, RAG over runbooks, tickets, and telemetry, agentic remediation, and AI-assisted engineering workflows — from prototype through production with evaluation, guardrails, and cost controls.
• Define AI governance with security and compliance: data residency, PII handling, prompt and output logging, human-in-the-loop gates, and model/version change control for government clients.
• Drive urgency through time-boxed delivery: bias to 2–4 week proof points, kill or scale decisions on evidence, and visible burn-down of the riskiest unknowns first.

Required qualifications to be successful in this role

Must to Have Skills

• Azure platform depth (primary cloud): AKS at multi-tenant production scale, Azure networking (VNet, Private Link, Front Door, App Gateway), Entra ID, Azure Policy and landing-zone/Well-Architected design, autoscaling, tenancy isolation, and cost management (FinOps, reservations, tagging, chargeback).
• Azure services breadth: Azure Monitor and Log Analytics (KQL), Managed Grafana, Key Vault, Storage, API Management, Functions and Container Apps, Service Bus/Event Hubs, Azure SQL, and DevOps/GitHub integration. • Observability: Understanding of OpenTelemetry-based stacks (traces, logs, metrics), sampling and retention economics, and alert tuning at scale.
• CI/CD and GitOps: Understanding of GitHub Actions, Argo CD, incremental builds, and policy-as-code gates (OPA, Kyverno, Conftest) with container signing and scanning.
• Infrastructure-as-code and configuration management: Terraform/OpenTofu, Ansible, and configuration-in Git with drift detection.
• Identity and security: Understanding of SSO, RBAC, and JIT access patterns (Entra ID, Keycloak); secrets and certificate lifecycle automation (Key Vault, cert-manager, ACME).
• AI and GenAI fundamentals: LLM capabilities and limits, prompt engineering, context engineering, structured output and function/tool calling, fine-tuning vs. prompting trade-offs, token and inference cost management, and latency/quality trade-offs.
• RAG and knowledge systems: chunking and embedding strategies, vector and hybrid search (Azure AI Search, pgvector, or equivalent), re-ranking, grounding and citation, retrieval evaluation, and mitigation of hallucination and data leakage.
• Agentic and AI-assisted operations: multi-step agents and orchestration frameworks (LangChain/LangGraph, Semantic Kernel, or equivalent), Model Context Protocol (MCP) style tool integration, and evaluation and observability of AI systems (eval harnesses, golden datasets, tracing, drift monitoring).
• Understanding of Azure AI stack: Azure OpenAI / Azure AI Foundry, Azure AI Search, model deployment and quota management, private networking for AI endpoints, content safety, and responsible-AI controls.
• Hands-on engineering ability: comfortable in Python (and/or Go or TypeScript), Git-based workflows, SQL/KQL, and shipping working prototypes without waiting for a team to be assigned.
• Program tooling fluency: PI and agile planning at scale, RICE or equivalent prioritization, and dependency and risk management.

Good-to-Have Skills:

• 12+ years in technology, including 5+ years running multi-team technical programs (8–15 initiatives) as a TechPM, program lead, or platform delivery lead.
• Operations transformation: moving support and operations organizations from reactive firefighting to problem management and SRE practices.
• FOSS-biased platform strategy with explicit commercial buy triggers; hands-on vendor MSA negotiation.
• Developer platform delivery (e.g., Backstage) and production delivery of AI-assisted operations — LLM assisted triage, RAG-backed knowledge and runbook assistants, and self-healing workflows with measured deflection.
• Azure certifications or equivalent demonstrated depth (e.g., AZ-305 Solutions Architect, AZ-400 DevOps Engineer, AI-102 Azure AI Engineer).
• Active hands-on builder — personal projects, open-source contributions, or side builds using AI tooling; genuinely passionate about development and fast iteration.
• Presenting to executive leadership: funding asks, program reviews, and candid at-risk status narratives.

Preferred candidate profile