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 adoption
Partner 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 metrics
Own the AI product vision, strategy, and multi-quarter roadmap, prioritizing a portfolio of AI and generative-AI initiatives by business value, feasibility, and risk
Serve as product owner for AI products: define target outcomes, write user stories and acceptance criteria, groom the backlog, and lead sprint prioritization and release planning
Run product discovery with business, functional, and end-user stakeholders to validate problems, shape target-state workflows, and translate needs into clear, well-scoped product requirements
Define product success metrics and KPIs (adoption, value realized, accuracy, and efficiency), own the business case, and track and report realized benefits to leadership
Manage the end-to-end AI product lifecycle from concept and pilot through scale-up, iteration, and eventual retirement, balancing scope, timeline, cost, and quality
Drive go-to-market, change management, and user enablement for AI products, including onboarding, training, documentation, and adoption campaigns that maximize usage and impact
Manage AI use-case intake and the product portfolio, applying prioritization frameworks and communicating trade-offs, sequencing, and decisions to senior stakeholders
Own 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 data
Establish reference architectures and reusable patterns hybrid deterministic/non-deterministic frameworks, prompt-engineering standards, and shared services and APIs that accelerate delivery across teams
Define 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 deployment
Build evaluation harnesses, guardrails, and automated testing that gate every release including groundedness and quality checks, content-safety controls, and business-owned acceptance criteria
Implement monitoring, observability, and feedback loops for AI products in production tracking accuracy, cost, latency, drift, and user adoption
Establish 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 operate
Establish 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 controls
Ensure all AI products are secure, auditable, and compliant with data-privacy, security, and regulatory requirements before production release
Partner with data governance and security teams to define guardrails for approved models, data access, and human-in-the-loop review
Recruit, 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 levels
Build and lead delivery PODs, hand-picking talent and shaping team structure to match program demand
Conduct regular design reviews, code reviews, and peer-learning sessions to maintain quality and grow technical depth across the team
Manage vendors and delivery partners end-to-end including selection, contracting, and performance to secure the strongest talent for each program
Manage 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 components
Contribute 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 organization
Contribute to cross-functional reviews of delivery metrics, adoption, and business value

Qualifications:

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 experience
10+ 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 impact
Hands-on as a product owner or (technical) product manager for AI or data products, including roadmap ownership, backlog Management, and stakeholder-driven prioritization
Deep hands-on expertise with LLMs (e.g., OpenAI, Claude, Gemini) and GenAI patterns: agentic and multi-agent architectures, RAG, prompt engineering, and evaluation
Strong 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 monitoring
Experience establishing Responsible-AI and governance practices for AI system
Demonstrated success building and scaling teams and/or delivery PODs, including hiring, mentoring, and vendor management
Excellent communication and stakeholder-management skills, with the ability to translate business problems into deployed AI solutions and to communicate outcomes to senior leadership

Preferred skills:

Experience in medtech, life sciences, healthcare, pharma, or other regulated industries
Experience 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 ecosystem
Familiarity with Responsible-AI / GRC frameworks
Experience standing up or scaling a Global Capability Center (GCC) or offshore AI/analytics team

Senior Manager - AI Product / Portfolio Management