About the Opportunity
A leading enterprise organization is seeking a senior AI/ML Technical Lead / AI Platform Architect to drive the design, engineering, and evolution of a next-generation enterprise AI platform.
This role is ideal for a technology leader who started as a hands-on software engineer and has progressed into AI/ML architecture and platform leadership.
The ideal candidate combines deep software engineering expertise with modern AI/GenAI platform experience and has successfully built scalable solutions from the ground up.
This individual will serve as a player-coach, remaining technically engaged while leading engineering initiatives and mentoring a growing team.
Key Responsibilities
- Define and evolve enterprise AI platform architecture, developer frameworks, SDKs, reusable services, and best practices.
- Design and implement scalable AI platform capabilities supporting model access, orchestration, governance, security, and operational excellence.
- Lead development of AI/GenAI services including model serving, retrieval-augmented generation (RAG), agent architectures, vector search, context management, and tool integration.
- Establish standards for AI model lifecycle management, versioning, promotion workflows, governance, and auditability.
- Build observability, monitoring, evaluation, and traceability frameworks for AI applications and agentic workflows.
- Implement responsible AI, security, data privacy, identity management, and policy enforcement controls.
- Drive cloud-native platform engineering practices, including Kubernetes, Infrastructure as Code, CI/CD, automation, reliability engineering, and operational readiness.
- Conduct architecture reviews, write and review production-grade code, and guide critical technical decisions.
- Evaluate technology solutions and make strategic build-versus-buy recommendations.
- Partner with product, security, engineering, and data science teams to accelerate AI adoption across the organization.
- Build, mentor, and lead a high-performing AI platform engineering team while remaining hands-on technically.
Required Qualifications
- 10+ years of software engineering, platform engineering, distributed systems, or infrastructure experience with increasing technical leadership responsibilities.
- Proven experience designing and delivering enterprise AI, GenAI, MLOps, Data Platforms, Developer Platforms, or AI Engineering Platforms in production environments.
- Strong hands-on development experience with Python and at least one of:
- Java
- Go
- TypeScript
- Strong foundation in:
- Microservices
- APIs
- Distributed Systems
- Secure Software Development Lifecycle (SDLC)
- Code Reviews and Testing Methodologies
- Experience with:
- Generative AI Platforms
- Large Language Models (LLMs)
- RAG Architectures
- AI Agents & Agent Frameworks
- Model Governance & Lifecycle Management
- AI Observability & Evaluation
- Prompt Management & Guardrails
- Deep experience with:
- Kubernetes
- Docker/Containers
- Infrastructure as Code (Terraform, CloudFormation, etc.)
- CI/CD Pipelines
- Cloud Platforms (AWS, Azure, or Google Cloud Platform)
- Secrets Management
- Reliability Engineering & SRE Practices
- Ability to communicate effectively with executive leadership and technical stakeholders.
- Bachelor's degree in Computer Science, Engineering, or related discipline (or equivalent experience).
Preferred Background
- Experience supporting large-scale enterprise digital transformation initiatives.
- Combination of enterprise and high-growth/startup experience highly preferred.
- Demonstrated success building new products, platforms, or engineering capabilities from the ground up.
- Experience within highly regulated industries is a plus.
- Prior leadership experience managing and mentoring engineering teams.
Ideal Candidate Profile
We are seeking a builder and innovator who can bridge software engineering, cloud architecture, and AI/ML technologies.
The ideal candidate has a strong software development background, understands how to scale modern platforms, and can translate complex business challenges into secure, reliable, and enterprise-ready AI solutions.