Overview

This is a hands-on platform engineering role focused on the systems, patterns, environments, controls, and automation required for production AI/ML delivery.

Responsibilities

• As an AI/ML Platform Engineer, you will be at the heart of building, operating, and evolving the cloud-native AI/ML platform that powers data, analytics, artificial intelligence, and agentic capabilities across the organization.
• You will own critical platform components end-to-end — from containerized workloads, streaming pipelines, networking, security, and CI/CD automation, through ML/API gateway and multi-provider model integration, to Model Context Protocol (MCP) tooling and Agent-to-Agent (A2A) communication patterns.
• You will solve complex, operationally important engineering challenges by filtering and prioritizing inputs from a wide range of internal and external sources, drawing on an in-depth understanding of how platform sub-functions work together.
• Acting as a key technical liaison across Cloud, Cybersecurity, Governance, and adjacent engineering teams, you will ensure the platform remains aligned with enterprise standards and emerging best practices. Your decisions will be guided by resource availability and organizational objectives, and you will contribute to defining team responsibilities, engineering standards, and platform governance practices that raise the quality bar across closely related teams.
• You will work closely with AI/ML engineers and Data scientists to develop, deploy, and monitor machine-learning models and AI services at production scale, working with a pragmatic, reliability-first mindset that directly shapes the technical direction of a platform driving meaningful business outcomes.

Qualifications

• AI/ML, MLOps & Agentic Platforms
• Hands-on experience with ML/API gateway technologies such as LiteLLM or comparable solutions, and multi-provider integration across major cloud AI platforms including Azure OpenAI, Azure AI Foundry.
• Practical experience with MCP (Model Context Protocol) concepts — tools, resources, prompts, and server lifecycle management — and familiarity with Agent-to-Agent (A2A) communication patterns such as agent discovery and task delegation.
• Comfortable working with agentic AI frameworks (such as LangGraph, LlamaIndex or comparable technologies) to support the deployment and operation of agent workloads.
• Familiarity with observability and monitoring tools used in AI/ML environments, such as CloudWatch, Prometheus, Grafana, or LLM-specific evaluation tooling.
• Engineering & Automation
• Proficiency developing production-grade backend services in Python using asynchronous programming frameworks; experience with Rust would be advantageous.
• Ability to perform performance testing and troubleshooting for APIs and platform services, with a focus on scalability and reliability engineering.
• Exposure to agent memory or state management, Envoy Proxy, mTLS, or API security hardening.
• Databricks platform engineering experience, including workspaces, clusters/serverless, Unity Catalog, MLflow, model serving, jobs/workflows, permissions, and cost controls.
• Security & Identity
• Confident implementing platform security — Transport Layer Security, secrets management, access controls, and standard authentication flows.
• Working knowledge of API security practices applied within established guidelines.
• Leadership & Ways of Working
• Demonstrated ability to lead junior team members, set team priorities, and drive platform quality — balancing hands-on technical depth with team accountability and a focus on scalable, well-governed engineering.
• A collaborative team player who communicates clearly, follows established processes, and is comfortable seeking guidance when navigating new or complex challenges.

Education

• 5 or more years of relevant industry experience in software, cloud, or platform engineering, including large language model operations (MLOps) in a production environment.
• Degree in Computer Science, Software Engineering, or a related field, or equivalent professional experience.