Job Description

We are hiring an Technical Program Lead to drive enterprise-grade AI initiatives from concept to production. This is a hands-on leadership role that blends technical architecture, program execution, and stakeholder governance to deliver measurable outcomes on complex AI programs.


You will translate business needs into a buildable AI solution blueprint, align data/app/integration dependencies, guide cross-functional teams through iterative delivery, and ensure production readiness across security, reliability, and operational excellence. The scope spans GenAI and classic AI/ML use cases, including agents, RAG, document intelligence, analytics, and automation--integrated into enterprise applications and cloud ecosystems.


This role requires strong stakeholder management, crisp communication for exec audiences, and practical understanding of the AI delivery lifecycle (planning, build, validation, deployment, monitoring) to ensure realistic plans and high-quality outcomes. You will also enable high-performing teams through servant leadership, metrics-driven delivery, and proactive risk/dependency management--often across global time zones.



Solution leadership and architecture ownership
Convert problem statements into a solution blueprint: data flows, system boundaries, integration approach, and deployment model. Understand reference patterns for enterprise AI (e.g., RAG + vector search, tool-using agents, document processing pipelines, analytics + automation). Make pragmatic technical decisions that balance speed, cost, risk, and long-term maintainability.
2) End-to-end execution and delivery governance
Own delivery plan, milestones, dependencies, and release readiness across multiple workstreams (data, app, integration, AI, security). Drive execution discipline: scope control, change management, risk/issue tracking, dependency resolution, and delivery predictability. Ensure the team builds the "right thing" (value) and builds it "the right way" (quality, security, reliability).
3) AI quality, evaluation, and safety gates
Define AI acceptance criteria: accuracy/quality metrics, evaluation approach, test datasets, and human-in-the-loop needs where required. Suggest guardrails for safe usage: prompt/tool policies, data access controls, logging/traceability, and responsible AI checks. Ensure reproducibility and versioning across data, prompts/configs, and model components.
4) Production readiness and operational excellence
Drive go-live readiness across observability, performance, cost controls, rollback strategy, SLAs/SLOs, and incident response. Partner with platform/infra teams for deployment, networking, identity/SSO integration, secrets management, and environment hardening. Ensure monitoring covers both classic system health and AI-specific signals (drift, failure modes, response quality).
5) Stakeholder management and executive communication
Act as the single accountable owner for status, trade-offs, and decisions--communicating clearly to technical and business leaders. Produce crisp executive updates: progress, risks, mitigations, decisions needed, and next milestones. Align multiple stakeholders across regions/time zones and keep teams moving despite ambiguity.
6) Reusable assets and continuous improvement
Co-own reusable playbooks, templates, and reference architectures to accelerate future AI delivery. * Capture learnings post-release and drive measurable improvements in speed, quality, and reliability across programs.