Principal Product Manager

About Providence

Providence, one of the US’s largest not-for-profit healthcare systems, is committed to high quality, compassionate healthcare for all. Driven by the belief that health is a human right and the vision, ‘Health for a better world’, Providence and its 121,000 caregivers strive to provide everyone access to affordable quality care and services.

Providence has a network of 51 hospitals, 1,000+ care clinics, senior services, supportive housing, and other health and educational services in the US.

Providence India is bringing to fruition the transformational shift of the healthcare ecosystem to Health 2.0. The India center will have focused efforts around healthcare technology and innovation, and play a vital role in driving digital transformation of health systems for improved patient outcomes and experiences, caregiver efficiency, and running the business of Providence at scale.

Why Us?

Best In-class Benefits

Inclusive Leadership

Reimagining Healthcare

Competitive Pay

Supportive Reporting Relation

About Us

We are a technology-driven organization building enterprise-scale platforms that power modern Software Engineering and AI Engineering. Our mission is to accelerate innovation by embedding Artificial Intelligence directly into software engineering workflows, developer platforms, and business operations.

We believe AI should not exist as separate experiments or disconnected tools. Instead, AI must be integrated into the core engineering ecosystem with the same standards of reliability, scalability, governance, observability, and operational excellence expected from any production platform.

Our platform strategy spans both:

Software Development Lifecycle (SDLC)

AI Development Lifecycle (AIDLC)

We are investing heavily in AI-powered engineering platforms, developer productivity, automation, agentic workflows, reusable platform services, and enterprise AI governance.

Role Overview

We are looking for an experienced Product Manager to lead the strategy, vision, and execution of next-generation Engineering and AI Platforms.

This role sits at the intersection of:

Platform Engineering

Software Engineering

Cloud Engineering

AI Engineering

Developer Experience

Enterprise Architecture

Production Reliability

You will own products and platforms that enable engineers, architects, data scientists, AI engineers, and business teams to build, deploy, operate, and scale software and AI systems efficiently and securely.

You will help shape the future state where Software Development Lifecycle (SDLC) and AI Development Lifecycle (AIDLC) converge into a unified engineering ecosystem.

Key Responsibilities

Product Strategy & Vision

Define and drive platform strategy for enterprise engineering ecosystems.

Develop and execute multi-year product roadmaps across Software Engineering and AI Engineering platforms.

Identify opportunities to leverage AI to improve developer productivity, engineering efficiency, platform operations, and business outcomes.

Build compelling product visions aligned with enterprise technology strategy.

Software Engineering Platforms

Own and evolve platforms including:

Internal Developer Platforms (IDP)

API Management Platforms

Microservices Ecosystems

CI/CD Platforms

DevOps Toolchains

Developer Experience Platforms

Platform Observability Solutions

Engineering Productivity Platforms

Drive:

Developer self-service capabilities

Platform standardization

Engineering automation

Accelerated software delivery

Operational excellence

AI Engineering Platforms

Lead product strategy and execution for:

Enterprise LLM Platforms

AIDLC Platforms

Agentic AI Platforms

AI Evaluation Frameworks

Prompt Engineering Platforms

RAG (Retrieval-Augmented Generation) Ecosystems

Multi-Agent Systems

Model Observability Platforms

AI Governance Services

Drive adoption of reusable AI capabilities and platform services across the organization.

SDLC + AIDLC Convergence

Establish a unified engineering operating model by integrating AI directly into software engineering workflows.

Examples include:

AI-driven requirements generation

Architecture assistants

Automated code generation

AI-assisted testing

Security automation

Release intelligence

Operational copilots

AI-driven incident management

Champion the integration of AI into:

Development workflows

Platform engineering

DevSecOps

Cloud operations

Software quality processes

Productization of Platform Capabilities

Lead the development of reusable platform assets including:

APIs

SDKs

Frameworks

Shared libraries

Automation services

AI agents

Platform accelerators

Focus on:

Reusability

Consistency

Governance

Scalability

Enterprise adoption

Platform Governance

Define and drive governance frameworks covering:

Software Systems

Security

Compliance

Availability

Reliability

Resiliency

Scalability

AI Systems

Responsible AI

Model governance

Prompt governance

Data privacy

Explainability

Risk management

Human oversight

Engineering & AI Metrics

Define measurable success outcomes and platform KPIs.

Software Engineering Metrics

Track and improve:

Deployment Frequency

Lead Time for Changes

Change Failure Rate

Mean Time To Recovery (MTTR)

Platform Adoption

Developer Productivity

Service Reliability

AI Engineering Metrics

Track and improve:

Accuracy

Response Quality

Hallucination Rate

Cost Per Request

Token Consumption

Retrieval Accuracy

Latency

Automation Coverage

AI Adoption

Agent Effectiveness

Use metrics to drive investment decisions and roadmap prioritization.

Cross-Functional Leadership

Partner closely with:

Engineering Leaders

AI/ML Engineers

Platform Architects

DevOps Teams

Security Teams

Data Teams

Product Leaders

Executive Leadership

Build alignment across business and technology stakeholders.

Production Excellence

Ensure production-ready platforms through strong operational practices.

Drive:

Reliability Engineering

Platform Observability

Capacity Planning

Cost Optimization

AI Monitoring

Incident Response

Operational Governance

What Your Day Could Look Like

You may be:

Working with Engineering Teams

Reviewing API designs

Evaluating platform architectures

Discussing microservice scalability

Prioritizing platform investments

Collaborating with AI Teams

Designing RAG architectures

Evaluating agent workflows

Reviewing model evaluation strategies

Defining AI governance requirements

Reviewing Production Telemetry

Software Systems:

Latency

Error rates

Throughput

Availability

AI Systems:

Hallucinations

Drift

Retrieval performance

Inference latency

Token consumption

Driving Strategic Decisions

Balancing:

Innovation vs Stability

Speed vs Governance

Build vs Buy

Cost vs Performance

Standardization vs Flexibility

Managing Platform Priorities

Making roadmap decisions across:

Engineering productivity

Platform modernization

AI adoption

Technical debt reduction

Reliability improvements

Required Qualifications

Experience

8+ years in Product Management, Platform Management, Engineering Leadership, or related areas.

Experience delivering enterprise-scale software products and platforms.

Proven track record building engineering platforms or developer-focused products.

Experience working with AI/ML-powered products and platforms.

Technical Knowledge

Strong understanding of:

Software Engineering

APIs

Microservices

Distributed Systems

Cloud Native Architectures

Platform Engineering

Site Reliability Engineering

DevOps Practices

CI/CD Pipelines

AI Engineering

Large Language Models (LLMs)

Retrieval-Augmented Generation (RAG)

Agentic AI Systems

Prompt Engineering

Model Evaluation

AI Governance

AI Observability

AIDLC Frameworks

Preferred Qualifications

Experience with:

Azure AI Services

OpenAI Ecosystems

Kubernetes

Service Mesh Architectures

Internal Developer Platforms

Multi-Agent Platforms

AI Governance Frameworks

Enterprise Architecture

Engineering Productivity Platforms

MBA or advanced technical degree preferred but not required.

Leadership Competencies

We are looking for someone who:

Thinks in platforms, not individual features

Drives data-informed decisions

Challenges assumptions

Balances innovation with operational excellence

Influences without direct authority

Communicates effectively with executives and engineers

Understands both customer outcomes and technical realities

Thrives in ambiguity and complexity

Providence’s vision to create ‘Health for a Better World’ aids us to provide a fair and equitable workplace for all in our employment, whether temporary, part-time or full time, and to promote individuality and diversity of thought and background, and acknowledge its role in the organization’s success. This makes us committed towards equal employment opportunities, regardless of race, religion or belief, color, ancestry, disability, marital status, gender, sexual orientation, age, nationality, ethnic origin, pregnancy, or related needs, mental or sensory disability, HIV Status, or any other category protected by applicable law. In furtherance to our mission in building a more inclusive and equitable environment, we shall, from time to time, undertake programs to assist, uplift and empower underrepresented groups including but not limited to Women, PWD (Persons with Disabilities), LGTBQ+ (Lesbian, Gay, Transgender, Bisexual or Queer), Veterans and others. We strive to address all forms of discrimination or harassment and provide a safe and confidential process to report any misconduct.

Contact our Integrity hotline also, read our Code of Conduct.