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19d ago

Lead Principal Machine Learning Engineer

Oracle San Francisco, CA

San Francisco, CA

19d ago

Full time Senior Agent Orchestration AI

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Job Description

Lead technical ownership of OCI AI platform capabilities and enterprise, production-grade agentic AI systems on Oracle Cloud Infrastructure (OCI).

Responsibilities

Act as the senior technical owner of OCI AI platform capabilities, covering agent execution, inference systems, model serving, AI workflow orchestration, evaluation, and observability.
Design and deliver scalable agentic AI systems that can reason, plan, use tools, execute workflows, orchestrate multi-step tasks, and support safe human-in-the-loop escalation.
Develop production-grade services for tool calling, agent memory, context management, MCP 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.
Guide technical strategy across infrastructure, platform, security, data, and application engineering teams, translating broad goals into multi-quarter plans with measurable 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.
Assess 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 equivalents.
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 for delivered systems.

Requirements

Advanced degree (PhD, MS, or BS with equivalent practical experience) in computer science, AI/ML, engineering, or related field.
12+ years of professional software engineering experience with ownership of production systems, or equivalent impact at senior staff / principal level.
Proven track record as a 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.
Strong understanding of LLM application patterns including prompt design, structured outputs, function/tool calling, context management, RAG, memory, tool safety, and evaluation.
Proficiency in Python with ability to contribute production code, reviews, tests, and debugging in complex distributed environments.
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.
Strong 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 proven ability to lead technical direction, resolve ambiguity, and influence senior stakeholders.

Technologies

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

Preferred qualifications

Experience optimizing large-scale GPU inference or training workloads for latency, throughput, utilization, availability, and cost.
Experience building or operating model serving, inference gateways, agent runtimes, workflow engines, developer platforms, or internal AI productivity platforms.
Experience integrating AI systems with enterprise APIs, databases, cloud services, vector databases, embeddings, retrieval systems, identity systems, and policy enforcement layers.
Experience with LLM fine-tuning, long-context systems, reasoning models, model routing, caching, batching, quantization, or related generative AI research.
Experience building evaluation frameworks for agentic systems, including offline evals, online experiments, golden tasks, adversarial testing, regression gates, and observability dashboards.
Experience using AI-assisted software development tools such as Codex, Claude Code, Cursor, Copilot, or similar in large-scale engineering environments.
Track record of defining architectural standards, platform capabilities, or engineering practices adopted across multiple teams or organizations.
Experience in enterprise, cloud infrastructure, regulated, security-sensitive, or mission-critical environments.

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