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 ResponsibilitiesProduct 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 QualificationsExperience
- 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
