Lead Principal Machine Learning Engineer

Responsibilities

• Act as the senior technical owner of OCI AI platform capabilities, overseeing agent execution, inference systems, model serving, AI workflow orchestration, evaluation, and observability.
• Design and deliver scalable agentic AI systems that reason, plan, use tools, execute workflows, orchestrate multi-step tasks, and enable safe human-in-the-loop escalation.
• Develop production-grade services for tool invocation, agent memory, context management, Model Context Protocol integration, vector retrieval, multi-agent coordination, policy enforcement, and evaluation.
• Steer architecture across distributed services optimized for low latency, high throughput, GPU efficiency, reliability, cost, operability, and secure multi-tenant operation.
• Define service boundaries, APIs, data models, state management, consistency tradeoffs, failure modes, SLIs/SLOs, rollout strategies, and readiness criteria for AI platform services.
• Drive technical strategy across infrastructure, platform, security, data, and application engineering teams, translating broad goals into multi-quarter plans and milestones.
• Integrate AI agents securely and reliably with enterprise APIs, cloud services, databases, identity systems, secrets management, and external systems.
• Establish AgentOps and LLMOps practices for tracing, monitoring, evaluation suites, regression testing, experimentation, safety guardrails, prompt and tool versioning, and production reliability.
• Evaluate and operationalize emerging technologies in generative AI, agentic workflows, inference optimization, long-context systems, reasoning models, AI developer tooling, and agentic-first development.
• Promote engineering excellence through code and design reviews, test strategy, deployment automation, incident analysis, documentation, and AI assisted development using tools like Codex, Claude Code, Cursor, Copilot, or similar.
• Mentor staff and senior engineers, raise architectural standards, and influence OCI engineering practices without requiring direct management authority.
• Own critical production outcomes including reliability, performance, security posture, cost efficiency, and supportability of the systems delivered.

Requirements

• Bachelor's, Master's, or PhD in Computer Science, AI/ML, Engineering, or related field, or equivalent practical experience.
• 12+ years of professional software engineering experience with substantial ownership of production systems, or equivalent Senior Staff / Principal level impact.
• Proven track record as Staff, Senior Staff, Principal, or equivalent technical leader influencing architecture and execution across multiple teams.
• Deep experience designing, building, and operating high-scale distributed systems, cloud services, infrastructure platforms, or AI/ML platform services.
• Hands-on experience with production AI systems, agentic AI applications, autonomous workflows, tool-using agents, multi-step orchestration, or multi-agent systems.
• Practical experience with orchestration frameworks such as LangGraph, LangChain, CrewAI, AutoGen, LlamaIndex, or similar ecosystems.
• Deep understanding of LLM application patterns, including prompt design, structured outputs, function/tool calling, context management, RAG, memory, tool safety, and evaluation.
• Strong Python programming skills with ability to contribute production-grade code, reviews, tests, and debugging in complex distributed environments.
• Strong expertise with Kubernetes, Docker, cloud-native infrastructure, service-to-service communication, scalability, fault tolerance, observability, and performance analysis.
• Experience defining SLIs/SLOs, production readiness criteria, incident response practices, monitoring, tracing, experiments, and reliability programs for AI or distributed systems.
• Solid understanding of AI safety, governance, security, and operational risks for autonomous or semi-autonomous systems, including data handling, access control, auditability, and human accountability.
• Excellent written and verbal communication with demonstrated ability to lead technical direction, resolve ambiguity, and influence senior stakeholders.

Technologies

• Oracle Cloud Infrastructure (OCI)
• Python
• Kubernetes
• Docker
• LangGraph
• LangChain
• CrewAI
• AutoGen
• LlamaIndex
• Model Context Protocol (MCP)
• Codex
• Claude Code
• Cursor
• Copilot