AI Engineering Manager​/Delivery Lead; Remote

Position: AI Engineering Manager/ Delivery Lead (Remote) Location: Republic We are seeking an AI Engineering Manager / Delivery Lead to own end‑to‑end delivery execution for Agentic AI solutions within AI & Technology Solutions (ATS), Clinical AI & Technology Innovation (CAITI). This role is accountable for translating product intent and architectural direction into reliable, compliant, high‑quality delivery outcomes, by leading internal teams and external vendors. Operating as part of the Core Development Team, the Engineering Manager is the single point of accountability for delivery execution, owning sprint cadence, SDLC deliverables, delivery readiness, and release orchestration from Proof of Concept through Production. This role works in tight partnership with: AI Product Manager & Product Owner (value, scope, priorities) AI Solution Architect & Tech Lead (architecture, patterns, technical direction) Program Manager (governance, cross‑program alignment, escalation) while retaining clear decision authority over delivery execution.

Key Responsibilities

• Own end‑to‑end delivery execution for Agentic AI solutions from Proof of Concept through Production, ensuring predictable, high‑quality outcomes.
• Translate product scope and architectural direction into clear delivery plans, sprint cadence, and execution milestones.
• Drive day‑to‑day engineering delivery, including dependency management, issue resolution, and removal of execution blockers.
• Ensure delivery readiness across environments, CI/CD, quality gates, validation, and release governance.
• Coordinate and orchestrate internal teams and vendors to deliver as a single, integrated delivery unit.
• Partner closely with Product, Architecture, QA, Validation, and Program Management to ensure delivery aligns with regulatory, quality, and governance expectations.
• Provide transparent delivery reporting, proactively surface risks, and escalate issues with clear options and trade‑offs.
• Support release and rollout execution (pilot to scale) and ensure effective handover to operations.
• Lead and mentor a multidisciplinary engineering team (AI engineers, data engineers, platform engineers), providing technical guidance, career development, and performance management.
• Act as delivery owner for vendor‑contributed work, including:

1) Task integration, 2) Quality expectations, 3) Delivery timelines.

• Ensure vendor outputs meet IQVIA standards and are integrated seamlessly into CI/CD and release processes.
• Surface delivery risks related to vendor dependency early and clearly.

Key Qualifications Experience

• 7+ years in engineering delivery roles, with demonstrated ownership of end‑to‑end delivery in complex systems.
• Proven experience delivering Agentic AI, AI‑enabled, or data‑intensive platforms, including systems that incorporate reasoning, orchestration, retrieval, or automation components.
• Experience delivering AI solutions in regulated or highly governed enterprise environments, with accountability for production readiness, auditability, and lifecycle controls.
• Demonstrated ability to coordinate cross‑functional teams and external vendors delivering agent‑based solutions across prototype, MVP, and production stages.

Technical & Delivery Background

• Bachelor’s degree with 10+ years of experience, or Master’s degree with 7+ years of experience in Computer Science, Information Systems, or a related technical field.
• Strong understanding of Agentic SDLC / ADLC concepts, including agent lifecycle management, phased progression.
• Experience delivering systems that require agent evaluation, monitoring, and feedback loops, including performance, reliability, and behavioral drift considerations.
• Ability to engage credibly with Solution Architects, Tech Leads, and AI teams on agent feasibility, orchestration complexity, dependency sequencing, and delivery risk.
• Practical understanding of CI/CD for agentic systems, including environment readiness, controlled releases, rollback strategies, and post‑deployment monitoring.
• Proficiency in programming languages such as Python, Java, or Scala, with strong understanding of data structures and algorithms.

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