Principal AI Agent ML Software Engineer (OCI)

The Principal AI Agent / ML Software Engineer is a Senior Staff-level hands-on technical leadership role responsible for defining building and operating next-generation AI systems on Oracle Cloud Infrastructure (OCI). This person will set architecture and engineering direction for production-grade agentic AI platforms autonomous workflows scalable inference infrastructure and enterprise AI applications used in large-scale business-critical environments.

This role requires a proven engineer who can translate ambiguous product and platform goals into durable technical strategy lead multi-team execution without direct authority and remain deeply hands-on in design code reviews operations and incident follow-up. The ideal candidate combines deep distributed systems experience with practical AI-native engineering including orchestration of LLMs tools APIs memory retrieval evaluation guardrails and cloud services. The expectation is to ship scale and operate reliable secure observable and cost-aware AI platform systems while raising the technical bar for engineers across the organization

Responsibilities

• Serve as a senior technical owner for OCI AI platform capabilities including agent execution inference systems model serving AI workflow orchestration evaluation and observability.
• Design architect and deliver scalable agentic AI systems capable of reasoning planning tool use workflow execution multi-step task orchestration and safe human-in-the-loop escalation.
• Build production-grade services for tool calling agent memory context management Model Context Protocol (MCP) integration vector retrieval multi-agent coordination policy enforcement and evaluation.
• Lead 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 operational readiness criteria for AI platform services.
• Drive technical strategy across infrastructure platform security data and application engineering teams converting broad goals into executable multi-quarter plans and 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 eval suites regression testing experimentation safety guardrails prompt/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.
• Drive engineering excellence through code reviews design reviews test strategy deployment automation incident analysis documentation and AI-assisted development practices using tools such as Codex Claude Code Cursor Copilot or similar systems.
• Mentor Staff and senior engineers raise architectural standards and influence engineering practices across OCI without requiring direct management authority.
• Own critical production outcomes including reliability performance security posture cost efficiency and supportability for the systems delivered.

Required Qualifications

• Bachelors Masters or Ph.D. in Computer Science AI/ML Engineering or a related field or equivalent practical experience.
• 6-10 years of professional software engineering experience including significant ownership of production systems; or equivalent experience demonstrating Senior Staff / Principal-level impact.
• 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.
• Deep understanding of LLM application patterns including prompt design structured outputs function/tool calling context management RAG memory tool safety and evaluation.
• Strong programming skills in Python and ability to contribute high-quality production 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.
• 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 demonstrated ability to lead technical direction resolve ambiguity and influence senior stakeholders.

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 emerging 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 systems 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.