Principal Software Engineer, Applied AI

চাকরির বিবরণ

The team that built Jeeva AI is building Digital Workers, a new suite of autonomous agents for identity operations and security inside Okta. The team works directly with Todd McKinnon, Okta's co-founder and CEO, with the opportunity to bring this product to Okta's 20,000+ customers. You will help shape the product with the ownership and pace of a startup and the resources of a public company.

You will build the harnesses and evaluations that make these workers reliable. They investigate risk, use tools, wait for approvals and complete tasks across enterprise systems. Bring depth in agent execution or model quality and help turn it into working software customers can trust.

Production builder : You have shipped AI software and know what changes when real users depend on it.

Careful evaluator : You investigate failures and use evidence to judge quality.

Strong owner: You follow an improvement through code, evaluation, release and customer use.

Principal: Set product engineering direction across workflows and implement the shared components that help the team ship faster.

Our core stack is Python FastAPI, AWS Step Functions, Amazon Bedrock AgentCore, PostgreSQL on Amazon RDS and pgvector. OpenAI and Anthropic model APIs are part of the integration plan.

We plan to add an agent harness and a dedicated analytics layer using ClickHouse or BigQuery. The harness choice is open. Experience with equivalent tools also counts.

দায়িত্ব

• Build the agent harness for planning, tool use, memory, context compaction, interruption and recovery during long-running work.
• Integrate OpenAI and Anthropic model APIs. Select models using measured quality, latency and cost, with clear failure handling.
• Evaluate existing harnesses and build only the missing pieces. Keep the harness connected to AWS workflow execution and approval state.
• Keep authorization and sensitive writes behind application policy checks. Require evidence of the resulting state before marking work complete.
• Build evaluation datasets and replay tests for valid findings, access decisions, app setup and AI agent onboarding.
• Measure false positives, missed risks, unsafe actions, completion rate and cost. Turn production failures into regression tests.
• Test prompt injection, stale evidence, conflicting instructions and unsafe tool use. Protect tenant boundaries in retrieval and memory.
• Improve prompts, retrieval, tools and model routing. Evaluate fine-tuning or other post-training methods when results justify them and data use is approved.

যোগ্যতা

• Strong Python engineering skills and experience shipping an LLM application that uses tools.
• Hands-on experience building agent workflows or evaluation systems for tool-using LLM applications. For harness work, relevant experience includes Claude Agent SDK, OpenAI Codex SDK, Strands Agents, LangChain Deep Agents, LangGraph or a custom implementation.
• Experience integrating model APIs, structured outputs and asynchronous services. Experience with OpenAI or Anthropic is especially relevant.
• Depth in agent execution or evaluation systems, with working code and failure analysis you can explain.
• Ability to distinguish a plausible model response from a supported finding or verified action.
• Open-source contributions, agent observability, security evaluations or synthetic evaluation data.
• Fine-tuning, preference data, post-training evaluation or model distillation using approved datasets.
• Identity and security workflows. Foundation-model research experience is optional.