Senior Manager - AI Platform Engineering [T500-30652]

ANSR; Bengaluru; On-site; INR 3,000,000 - 5,400,000; Full time

Job summary

ANSR MedTech Capability Center in Bengaluru is building an enterprise AI platform to support data workloads, model serving, and AI applications at scale. The Senior Manager will own architecture, platform build-out, and day-to-day operations in a regulated MedTech environment.

The role collaborates with applied AI, data engineering, cloud infrastructure, and governance teams to deliver a secure, cost-efficient, production-grade platform, driving observability, security, and FinOps across AI

Qualifications

• Bachelor’s degree in a quantitative field.
• 15+ years hands-on software, data, or platform engineering.
• Proven experience as a Data & AI solution architect for enterprise-scale architectures.
• Cloud AI/platform experience including MLOps, CI/CD, IaC, and release engineering.
• Strong Python, SQL, API/microservices design and observability for data/AI workloads.

Responsibilities

• Define target-state architecture for the AI platform, including data ingestion, feature stores, model serving, and API layers.
• Build and maintain shared platform services and reusable components.
• Establish MLOps foundations, evaluation harnesses, and release criteria for AI apps.
• Design enterprise data architecture across lakehouse, warehouse, and integration layers.
• Own end-to-end platform operations: capacity, performance, availability, and incident management.
• Manage vendors and senior stakeholders, communicating roadmap, risk, and cost to leadership.

Skills

Python

SQL

Distributed data processing

API & microservices design

Observability & MLOps

Cloud data platforms

FinOps / cost management

Stakeholder management

Education

Bachelor’s degree or above in computer science, engineering, information technology, or a related quantitative field

Tools

Databricks

Azure Data Factory

Azure ML/AI Foundry

AKS / Kubernetes

Terraform

Job description

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‑efficient, 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
• 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