Senior Software Engineer

Overview

Position: Senior Software Engineer
Domain: AI Agents
Experience: 5+ years
Location: Remote
Shift: IST
Duration: 6 months

Key Responsibilities

• 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 Skills

• 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.

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.