Software Engineer, Applied AI

About Us and the Role

At Sobek AI, we’re building secure AI infrastructure for agentic workflows in life‑sciences innovation networks and intergovernmental emergency response. Backed by $10M+ in grants and funding, we work with global, high impact partners on distributed workflows where reliability, security, and trust matter from day one. Our systems are already deployed in mission‑critical customer environments.

We’re hiring a Software Engineer to help build the production AI systems behind Sobek’s core offerings, sitting where agentic workflows meet enterprise data and trust boundaries.

This is a foundational role on the engineering team, so we’re looking for someone who has shipped AI systems used by real users and has the software judgment to harden them for sensitive data and scale performance. This means experience with defining clear access boundaries, measurable quality, failure handling, and debuggable interfaces.

While this is not a research role, it does require practical ML and LLM fundamentals. You should understand enough about how models are trained, evaluated, served, and deployed to make sound engineering decisions when building with them.

What You’ll Do

Build Production AI Workflows

• Build agentic workflows over enterprise and government data, with clear rules for what a model can see, what tools it can call, and when a human needs to review or approve an action.

• Design context and grounding systems that give models the right information at the right time without violating permissions or performance constraints.

• Work across backend services, APIs, async workers, data pipelines, internal tools, and product facing surfaces.

Engineer Reliable LLM Systems

• Build evals and feedback loops for model behavior and workflow outcomes.

• Own tracing and runtime visibility across models, context, tool calls, generated outputs.

• Debug failures from evidence: context, traces, tool responses, user review, production logs.

• Improve quality without ignoring latency, cost, or security.

Build Reusable AI Systems

• Create shared primitives for context assembly, grounding, tool use, and reviewable outputs.

• Build systems that turn domain specific AI behavior into product infrastructure rather than one off customer logic.

• Move quickly from prototype to production quality systems with founders and engineers.

Lead Through Ownership and Engineering Quality

• Take ownership of important product and platform surfaces without needing heavy direction.

• Write clean, maintainable code and create clear abstractions.

• Use tools like Claude Code, Codex, ChatGPT, Cursor, and similar systems to move faster, while applying the same standards to generated code as hand written code.

• This role requires treating LLMs not as black boxes to call, but as architectural components with failure modes and costs to manage.

About You

You have a track record of shipping production software and have built at least one AI product or workflow used by real users, ideally in an enterprise or scaled consumer environment.

You have strong software fundamentals and are fluent in Python and/or TypeScript . Our current stack spans React/TypeScript, Python services, gRPC, AWS, Kubernetes, Terraform, Snowflake, and Docker ; exact stack match is less important than range and judgment.

You are comfortable with ambiguity and accompanying ownership.

Strong candidates may also have:

• shipped production LLM or agentic workflows at an AI native startup, scaled AI product company, or serious applied‑AI team;

• built evals or feedback loops that caught real regressions;

• debugged production failures in agent workflows, especially around grounding, tool use, or model/runtime boundaries;

• built systems that operate over enterprise data with defined security boundaries;

• worked in domains where wrong answers have serious consequences, such as scientific, medical, legal, financial, or public sector workflows;

• owned meaningful product or platform surface area earlier than their title would suggest.

Details

• Compensation: $170 K – $230 K

• Location: Hybrid (Seattle, WA)

• Visa: We do not sponsor visas for this role at this time

• Benefits: Company‑paid health coverage (including dependents)

• Equity: Meaningful ownership for early engineers, with flexibility to extend for exceptional scope and impact.