[Hiring] AI Data Engineers @SPMR Advisors

Role Description

The Data + Automation team at our company 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

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
• 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

Qualifications

• 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

Requirements

• Databricks
• LLM Agents
• AI Agentic
• AWS
• Pyspark
• Unity Catalog
• Python Coding experience