Senior Manager - AI Product / Portfolio Management [T500-28315]

ANSR is hiring for one of its clients.About ANSR MedTech: ANSR MedTech Capability Center is a new global innovation hub being established in India for a Fortune 100 Fastest-Growing Company in the MedTech sector. Built in partnership with ANSR, the center draws on ANSR’s proven experience in establishing and scaling high-performance Global Capability Centers (GCCs) for leading global enterprises. ANSR MedTech center brings together world-class engineering, product, and technology talent to build next-generation healthcare platforms and solutions that power global operations.Our Vision:To build a next-generation MedTech capability center that powers global healthcare innovation. We envision: High-impact innovation hubs shaping global product and technology roadmaps Centers that go beyond support functions to drive core engineering and platform development Sustainable, scalable ecosystems that nurture world-class MedTech talent Capability centers that directly influence patient outcomes worldwide At its core, the ANSR MedTech Capability Center is about enabling innovation that touches lives at scale. Job Title: Senior Manager - AI Product & Portfolio Management Location: Bengaluru, India Job Summary: Senior Manager - AI Product Portfolio Management will lead a team of applied AI and generative-AI engineers and own the product management of AI solutions within the India COE, accountable for the vision, design, development, and production delivery of AI and generative-AI products, including LLM-powered assistants, agentic and retrieval-augmented (RAG) applications, and machine-learning models, that drive measurable business outcomes across the enterprise.This role reports to the Director of Applied AI and operates within a multi-disciplinary delivery organization collaborating closely with data scientists, data engineers, analytics engineers, and data governance professionals to take AI products from business problem to production, with the reliability, guardrails, and Responsible-AI standards required in a regulated MedTech environment.Key Responsibilities: Lead the design, development, and production delivery of applied AI and generative-AI products LLM-powered assistants, agentic workflows, RAG applications, and ML models from business-problem framing through deployment and adoptionPartner with business and functional leaders to identify high-value AI use cases, frame problems, and translate them into well-scoped, deliverable products with clear success metricsOwn the AI product vision, strategy, and multi-quarter roadmap, prioritizing a portfolio of AI and generative-AI initiatives by business value, feasibility, and riskServe as product owner for AI products: define target outcomes, write user stories and acceptance criteria, groom the backlog, and lead sprint prioritization and release planningRun product discovery with business, functional, and end-user stakeholders to validate problems, shape target-state workflows, and translate needs into clear, well-scoped product requirementsDefine product success metrics and KPIs (adoption, value realized, accuracy, and efficiency), own the business case, and track and report realized benefits to leadershipManage the end-to-end AI product lifecycle from concept and pilot through scale-up, iteration, and eventual retirement, balancing scope, timeline, cost, and qualityDrive go-to-market, change management, and user enablement for AI products, including onboarding, training, documentation, and adoption campaigns that maximize usage and impactManage AI use-case intake and the product portfolio, applying prioritization frameworks and communicating trade-offs, sequencing, and decisions to senior stakeholdersOwn end-to-end solution architecture for AI products, including multi-agent (Planner–Executor) designs, retrieval pipelines, tool orchestration, and integration of LLMs (e.g., OpenAI, Claude, Gemini) with enterprise dataEstablish reference architectures and reusable patterns hybrid deterministic/non-deterministic frameworks, prompt-engineering standards, and shared services and APIs that accelerate delivery across teamsDefine and enforce LLMOps and MLOps practices: CI/CD for AI applications, environment promotion across dev, QA, and prod, model and prompt versioning, and horizontally scalable deploymentBuild evaluation harnesses, guardrails, and automated testing that gate every release including groundedness and quality checks, content-safety controls, and business-owned acceptance criteriaImplement monitoring, observability, and feedback loops for AI products in production tracking accuracy, cost, latency, drift, and user adoptionEstablish coding standards, peer-review processes, quality gates, and definition-of-done criteria for AI engineering deliverables consistent with how ANSR MedTech’s established Data & AI teams operateEstablish and operate the Responsible-AI and governance pathway for AI and LLM usage on sensitive and regulated data spanning risk assessment, security testing, documentation, and compliance controlsEnsure all AI products are secure, auditable, and compliant with data-privacy, security, and regulatory requirements before production releasePartner with data governance and security teams to define guardrails for approved models, data access, and human-in-the-loop reviewRecruit, develop, and Manage performance of and lead a multi-disciplinary team of applied AI and GenAI engineers, ML engineers, and full-stack developers at varying experience levelsBuild and lead delivery PODs, hand-picking talent and shaping team structure to match program demandConduct regular design reviews, code reviews, and peer-learning sessions to maintain quality and grow technical depth across the teamManage vendors and delivery partners end-to-end including selection, contracting, and performance to secure the strongest talent for each programManage senior stakeholders across business, functional, and technology leadership, communicating progress, risks, and realized business impact Collaborate across data science, analytics engineering, and data engineering to share standards, patterns, and reusable componentsContribute to the COE’s shared library of reusable AI components, agent and RAG patterns, and platform services Participate in roadmap definition, sprint planning, and capacity alignment with the I&A organizationContribute to cross-functional reviews of delivery metrics, adoption, and business valueQualifications:Bachelors’ degree or above in computer science, data science, artificial intelligence, engineering, or a related quantitative field or a Bachelor’s degree with equivalent depth of hands-on experience10+ years of hands-on in AI/ML, data science, or software engineering, with at least 3–5 years leading technical teams delivering products to production Proven track record of delivering generative-AI and LLM products end-to-end from problem framing to production with measurable business impactHands-on as a product owner or (technical) product manager for AI or data products, including roadmap ownership, backlog Management, and stakeholder-driven prioritizationDeep hands-on expertise with LLMs (e.g., OpenAI, Claude, Gemini) and GenAI patterns: agentic and multi-agent architectures, RAG, prompt engineering, and evaluationStrong software and data engineering foundation: Python, SQL, and cloud data and AI platforms (Azure Databricks, Data Factory, Azure ML/Foundry, App Services or equivalent)Experience productionizing AI and ML with LLMOps/MLOps: CI/CD, model and prompt versioning, guardrails, evaluation harnesses, and monitoringExperience establishing Responsible-AI and governance practices for AI systemDemonstrated success building and scaling teams and/or delivery PODs, including hiring, mentoring, and vendor managementExcellent communication and stakeholder-management skills, with the ability to translate business problems into deployed AI solutions and to communicate outcomes to senior leadershipPreferred skills:Experience in medtech, life sciences, healthcare, pharma, or other regulated industriesExperience with agentic frameworks and orchestration, vector and retrieval stores (e.g., FAISS), and content-safety or guardrail tooling (e.g., Azure Content Safety)Experience with Databricks (SQL, Unity Catalog) and the broader Azure AI ecosystemFamiliarity with Responsible-AI / GRC frameworksExperience standing up or scaling a Global Capability Center (GCC) or offshore AI/analytics team