YOUR IMPACT
We are seeking a highly skilled Manager
to lead and orchestrate complex, multi-disciplinary AI and Generative AI programs
within the enterprise AI engineering organization.
This role demands a deep understanding of AI-driven product development lifecycles
, multi-agent orchestration
, and LLM-based system engineering
to effectively plan, govern, and deliver cutting-edge AI capabilities.
The ideal candidate combines program management excellence
, technical fluency in AI systems
, and the ability to translate strategic AI goals into structured execution
across engineering, data science, platform, and business enablement teams.
What The Role Offers
Program Leadership & Governance
- Own the
end-to-end delivery lifecycle
for enterprise AI programs including RAG systems, multi-agent workflows, and MCP/A2A-based architectures
and other latest cutting edge technologies. - Define program goals, KPIs, milestones, and delivery timelines in alignment with
AI strategy and enterprise roadmap
. - Drive
program governance
, risk management, and dependency tracking across multiple parallel AI initiatives. - Ensure consistent alignment between engineering execution and business value realization.
Cross-Functional Delivery Management
- Collaborate with
Principal AI Engineers, Solution Architects, and Product Owners
to translate complex AI designs into executable workstreams. - Manage cross-functional teams involving
AI Engineers, ML Ops, Data Engineers, and Platform Teams
for synchronized delivery. - Facilitate sprint planning, backlog refinement, and release management across distributed AI development squads.
- Coordinate
model deployment readiness
, inference optimization
, and guardrail validation
phases prior to production rollout.
AI Program Execution Excellence
- Implement
AI-centric delivery frameworks
integrating experimentation, validation, and continuous improvement cycles. - Ensure operational excellence in
GenAI system reliability, observability, and performance tracking
through data-driven dashboards. - Lead the adoption of
modular delivery frameworks
to support iterative releases of AI components. - Work closely with AI Engineering leadership to optimize
resource allocation
, infrastructure utilization
, and cost efficiency
.
Stakeholder Communication & Reporting
- Serve as the
single point of accountability
for all program-level communications and escalations. - Present delivery status, risk mitigations, and success metrics to executive leadership.
- Establish transparent communication channels across
engineering, product management, data governance, and compliance
. - Translate technical achievements into
business outcomes
, highlighting measurable ROI of AI programs.
AI Delivery Process Innovation
- Champion the adoption of
AI-specific program management tools
(e.g., LangSmith dashboards, LLMOps pipelines, model evaluation trackers). - Drive
process automation
in delivery tracking, documentation, and validation through intelligent agents. - Define
best practices for managing LLM lifecycles, versioning, and evaluation cycles
in enterprise environments. - Collaborate with AI leadership to
standardize frameworks, templates, and operational playbooks
for repeatable GenAI success.
- Lead the
AI transformation delivery layer
, driving execution excellence across all enterprise AI programs. - Collaborate with world-class AI engineers building the
next generation of agentic and RAG-based systems
. - Shape how enterprise-scale
GenAI initiatives
are planned, tracked, and delivered for measurable impact. - Work at the forefront of
AI program management
, setting new standards for delivery velocity, governance, and innovation
.
What You Need To Succeed
Education:
Bachelors or Masters in Computer Science, AI/ML, Information Systems, or Project Management. PMP, PMI-ACP, or equivalent certification preferred. Experience:
10-15 years of experience managing complex technology programs, with 3-5 years in AI/ML or GenAI environments
. - Proven experience in managing
AI or data-intensive programs
involving model development, RAG pipelines, and LLM-based architectures. - Strong understanding of
AI engineering principles
, multi-agent frameworks (LangGraph, Crew AI, ADK)
, and LLMOps
lifecycles. - Proficiency in
Agile/Scrum
and Scaled Agile
methodologies tailored to AI system development. - Experience leading teams working on
GenAI, RAG, or LLM-based solutions
in enterprise contexts. - Ability to manage
cross-functional dependencies
, budget, and timeline for multi-component AI initiatives. - Working knowledge of
CI/CD pipelines, Kubernetes deployments
, and AI observability metrics
. - Strong command over
risk management, change control, and compliance frameworks
in AI system deployments. - Hands-on familiarity with
AI platforms and model governance tools
(LangSmith, MLflow, Weights & Biases, Kubeflow). - Understanding of
AI cost and usage analytics
, telemetry (OTEL)
, and AI guardrail integration
for compliance. - Proven success in implementing
AI program dashboards
tracking success metrics (latency, accuracy, throughput, cost). - Experience managing
hybrid cloud or on-prem AI infrastructure
(AWS, Azure, GCP). - Strong background in
AI ethics, data privacy, and responsible AI delivery
practices. - Visionary leader who can
bridge technical and business priorities
in AI delivery. - Excellent communicator with the ability to
influence, align, and motivate
highly technical teams. - Analytical mindset with a
structured problem-solving approach
to manage uncertainty in evolving AI landscapes. - Strong interpersonal and organizational skills to drive high-impact outcomes in fast-paced environments.
- Passionate about
transforming enterprise operations through AI and intelligent automation
.
