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 Platform Engineering

Job Summary:

- The AI Platform Engineering Lead will own the architecture, build-out, and day-to-day operation of the enterprise AI platform, accountable for reference architecture, scale and observability, security, and FinOps for AI and data workloads.
- The role also carries Data & AI solution architecture accountability, defining target-state architecture and integration patterns across data platforms, model serving, agentic and RAG services, and enterprise applications, and setting the standards that delivery teams build to.
- This role reports to the Director of Applied AI and partners closely with applied AI and data engineering teams, cloud infrastructure, security, data governance, and Responsible AI to deliver a secure, cost-productive, production-grade platform in a regulated MedTech environment.

Key Responsibilities:

AI Platform Architecture & Engineering:

- Define and own the target-state architecture for the AI platform, covering data ingestion and storage, feature and vector stores, model training and serving, orchestration, agent frameworks, and API layers
- Build and maintain shared platform services and reusable components: retrieval pipelines, model gateways, prompt and evaluation services, guardrail integration, and authentication and entitlement enforcement
- Establish LLMOps and MLOps foundations, including evaluation harnesses, automated testing, quality gates, and release criteria for AI applications
- Set engineering standards, coding practices, peer-review processes, and definition-of-done criteria for platform deliverables

Data & AI Solution Architecture:





- Design enterprise data architecture across lakehouse, warehouse, streaming, and integration layers, including modeling standards, storage and compute patterns, and data contracts
- Design AI solution architectures including multi-agent and orchestration patterns, retrieval-augmented generation, tool and function calling, and integration of LLMs with enterprise data and applications
- Publish reference architectures, architecture decision records, blueprints, and reusable patterns that accelerate delivery and enforce consistency across teams
- Define integration architecture and API standards across enterprise systems, and guide build-versus-buy and vendor technology evaluations

AI Platform Operations & Reliability:

- Own end-to-end operations of the enterprise AI and data platform, including capacity, performance, availability, and incident and problem management. Own model and prompt versioning, artifact registries, deployment strategies (blue/green, canary), and rollback procedures for AI workloads
- Establish runbooks, change management, disaster recovery, and business continuity procedures for critical AI and data services
- Design and operate the observability stack for AI and data workloads: logging, metrics, tracing, alerting, and dashboards across pipelines, models, and applications. Instrument AI-specific telemetry, including accuracy and groundedness, latency, token consumption, cost per transaction, drift, guardrail triggers, and user feedback
- Establish data quality and pipeline reliability monitoring with automated detection, alerting, and remediation workflows
- Set cost guardrails, quotas, budget alerts, and tagging and allocation standards, and enforce them through platform controls

Security, Compliance & Governance:

- Embed security by design into platform architecture, including identity and access management, secrets management, network isolation, encryption, and least-privilege enforcement
- Ensure platform and AI workloads meet data privacy, security, and regulatory requirements, and are auditable and traceable end-to-end
- Partner with Responsible AI and data governance teams to operationalize guardrails, approved-model controls, registry integration, and human-in-the-loop enforcement at the platform layer
- Support audits, assessments, and validation activities with platform evidence, controls documentation, and remediation plans





Leadership & Stakeholder Management:

- Manage vendors and delivery partners end-to-end, including selection, contracting, and performance management
- Manage senior stakeholders across business, functional, and technology leadership, communicating roadmap, risk, cost, and platform value
- Collaborate across applied AI, data science, analytics engineering, and data engineering to share standards, patterns, and reusable components

Qualifications:

- Bachelor’s degree or above in computer science, engineering, information technology, or a related quantitative field
- 15+ years of hands-on experience in software, data, or platform engineering, including significant time in architecture and platform leadership roles
- Proven experience as a Data & AI solution architect, owning enterprise-scale architectures across data platforms, analytics, and AI/ML in production
- Experience building and operating a cloud AI/data platform at scale, including MLOps and LLMOps, CI/CD, infrastructure-as-code, and release engineering
- Deep expertise in cloud data and AI platforms (Azure: Databricks, Data Factory, Azure ML/AI Foundry, App Services, AKS, or equivalent on AWS/GCP)
- Strong hands-on foundation in Python, SQL, distributed data processing, and API and microservices design
- Experience designing and operating observability for data and AI workloads, including AI-specific evaluation and monitoring
- Demonstrated ownership of FinOps or cloud cost management, including forecasting, chargeback, unit economics, and optimization programs
- Practical experience with generative-AI architecture patterns: RAG, vector and retrieval stores, agentic and multi-agent orchestration, model gateways, and guardrails
- Demonstrated success building and scaling engineering teams and delivery PODs, including hiring, mentoring, and vendor management
- Excellent communication and stakeholder-management skills, with the ability to explain architecture, risk, and cost trade-offs to senior leadership

Preferred Skills:

- Experience in medtech, life sciences, healthcare, pharma, or another regulated industry, including validated or GxP environments
- Experience with Databricks (SQL, Unity Catalog, MLflow) and the broader Azure AI ecosystem
- Experience with Kubernetes, Terraform, and modern SRE practices including SLO-driven operations
- Familiarity with Responsible-AI / GRC frameworks and AI registry or model inventory integration
- Relevant cloud or architecture certifications (e.g., Azure Solutions Architect Expert, Databricks Data Engineer/Architect, TOGAF, FinOps Certified Practitioner)
- Experience standing up or scaling a Global Capability Center (GCC) or offshore AI/platform engineering team

📌 Senior Manager - AI Platform Engineering-30652] (Bengaluru)
🏢 ANSR
📍 Bengaluru