Senior AI Developer

Euna Solutions

2d•Remote

About The Position

Euna Solutions is seeking a talented Senior AI Developer to be a key participant in our ambitious AI transformation of our finance platform for local and state government agencies. We are raising the bar on every part of the development process with an intentional agentic focus. This isn’t an idea, it’s accelerating taking what we have already achieved to the next level and it’s a great opportunity for an innovative curious AI developer who wants to push the boundaries and learn and work with some incredible talent. The idea is to take this to the next level building applications with AI capabilities from the ground up. We need developers who know what good looks like, not just the code but all the other considerations necessary for building and designing enterprise grade software. You'll own the hard problems: orchestration patterns that hold up under real-world load, developer experiences that let your teammates move fast with confidence, and engineering decisions that turn ambiguous requirements into concrete, operable systems.

Requirements

7+ years of hands-on software development, with deep proficiency in at least one modern backend or full-stack language and breadth across the full SDLC
Understanding of agentic system design: orchestration, tool use, memory, retrieval, evaluation, and agent-to-agent communication — with the judgment to make the right architectural calls under real constraints
Some hands-on experience with production agentic tooling — orchestration frameworks (LangChain, LangGraph, CrewAI), LLM APIs (OpenAI, Anthropic, Bedrock), vector databases, and evaluation/observability platforms (LangSmith, PromptLayer)
Previous experience building scalable multi-tenant SaaS applications ideally in one of C#, Ruby, Rails, Elixir, Python or similar language.
Demonstrated experience delivering production, client-facing agentic solutions end-to-end — you've shipped agents real users depend on, navigated the hard operational problems, and iterated from prototype to stable production
Strong AWS experience (or similar on Azure) — comfortable with Lambda, ECS, S3, RDS, API Gateway, and CloudWatch; able to design production-grade cloud-native architectures that are observable and operationally sound
Deep grasp of SaaS architecture principles — you design systems others build on top of, and you understand the tradeoffs between speed, scalability, and maintainability at each layer
Proven track record building developer platforms and delivery systems — CI/CD pipelines, deployment automation, testing frameworks, and the processes that let teams ship AI features rapidly and safely
Strong background in background processing systems (Redis-backed job queues, schedulers) and high-scale data pipelines
Solid understanding of PostgreSQL schema design and performance optimization
Experience designing and operating production REST APIs (auth, versioning, validation)
Understanding in Event Driven Development.
Experience with search and discovery systems (full-text, semantic, or vector search)
Strong understanding of security best practices (auth, data isolation, secrets management)
A demonstrated AI-first development approach — concrete examples of using LLMs and agentic tools (Claude Code, Cursor, Copilot) across the full breadth of daily engineering work, not just AI features

Nice To Haves

Experience in government tech, public sector, or another compliance-heavy domain (auditability, data isolation, security reviews)

Responsibilities

Own the evolution of shared AI components, libraries, and platform capabilities the whole team builds on. You set the bar for reliability, testability, and developer experience — not as a reviewer, but as the person who ships it.
Design and build AI-powered product features end-to-end: LLM orchestration, prompt engineering workflows, evaluation, and observability. Carry work from requirements shaping through deployment, monitoring, and iteration.
Design and build the CI/CD patterns, deployment pipelines, observability tooling, and developer workflows that make shipping agents to production predictable and repeatable.
Lead the team on AI tooling adoption — coding assistants, agentic development workflows, evaluation frameworks. Set the patterns that give teammates real leverage, not just access.
Build and optimize the core SaaS platform systems AI features depend on: high-scale background processing, scalable APIs, ingestion pipelines, search and discovery, and PostgreSQL performance.
Articulate a clear engineering direction to your team and to stakeholders. Translate ambiguous problems into concrete plans. Build shared understanding across engineering and product so everyone's rowing the same way.
Actively grow the engineers around you. Run technical reviews, establish best practices, and create an environment where the team can move quickly without constantly second-guessing their decisions.