Design, build, and operate production LLM agents and supporting APIs using Python and Node/TypeScript. Develop tool-use loops, retrieval systems, orchestration, evaluation, and guardrails. Deploy services on AWS using Infrastructure as Code and CI/CD, while working with Databricks notebooks, Jobs, Unity Catalog, and Delta Lake. Contribute to code reviews, testing, documentation, and shared engineering standards. The role requires independently shipping production agents, RAG systems, and reliable software services.
ABOUT THE ROLE
The Data + Automation team at nCircle Tech builds the data and automation infrastructure behind decision
making across the business. Our platform runs on Databricks and AWS. We're hiring a strong software
engineer to design, build, and ship production services — including LLM-powered agents and the tooling
around them. This is a hands-on, code-first role.
WHAT YOU'LL WORK ON
● Designing, building, and productionizing LLM agents: tool-use loops, retrieval, orchestration,
evaluation, and guardrails
● Writing the production-grade services and APIs around those agents in Python and Node/TypeScript
● Deploying and operating on AWS — provisioning with IaC, wiring CI/CD, owning your code from
commit to production
● Contributing to a shared codebase with feature branches, code review, and Conventional Commits
REQUIRED
Software Engineering (the core)
● Strong Python and Node/TypeScript — writes clean, tested, production-grade code, not scripts
● Solid fundamentals: data structures, API design, error handling, testing, debugging real systems
● Feature branches and PRs; comfortable both giving and receiving code review; meaningful commit
messages
● Follows engineering standards (12-factor, testing, documentation) without hand-holding
AWS + IaC
● Real AWS experience — the environment our services run on
● Deploys with Infrastructure as Code (Terraform, CDK, or equivalent) rather than clicking through
consoles
● Comfortable with CI/CD, environment promotion, and AWS basics (S3, IAM, networking)
LLM Agents (non-negotiable)
This is a hard requirement, not a nice-to-have. We are a small team and cannot train this skill on the job
— you must already know how to build agents and be able to contribute to real work quickly.
● Has built and shipped LLM agents to production — not demos or prototypes; systems that ran and
were relied on
● Can explain the agent loop from experience: how the model decides to call a tool, observes the result,
and re-decides across multiple steps
1
Senior Software Engineer, AI Agents
● Has designed retrieval (RAG) end-to-end — chunking, indexing, hybrid or re-ranked retrieval — and
can justify each choice
● Has a real evaluation story: how they measured whether the agent was correct, and the worst failure
mode they hit in production and fixed
● Knows when NOT to use an agent — where a plain LLM call or deterministic code is the better answer
● Framework-agnostic — LangChain, CrewAI, Databricks Agent Framework, or raw tool-use loops all
fine; what matters is that you've shipped real agents
Databricks (working proficiency)
Our platform runs on Databricks, so this is required — but working proficiency, not mastery. You should
be able to build and ship in Databricks unaided; you don't need to be a Spark-tuning specialist.
● Comfortable building and running Databricks notebooks and Jobs (task chains, parameters,
schedules) end-to-end
● Understands the Unity Catalog three-part namespace (catalog.schema.table) and works within it
● Has written Delta Lake operations beyond basic reads — e.g. MERGE, overwrite, schema evolution
● Can navigate and debug an existing pipeline, not only write greenfield code
GOOD TO HAVE
● Deeper Databricks / Spark depth — performance tuning, advanced Unity Catalog governance
● MCP (Model Context Protocol) exposure
● Agent observability and tracing tools in production
● Construction, AEC, or project-based industry experience
● Jira
GENERAL BAR
● 5+ years writing code, not configuring tools
● Ships independently: scopes work, executes, communicates progress
● Can ramp on an unfamiliar system by reading docs and asking targeted questions
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