Software Developer

Software Developer

AI Developer

Skills Required:

Python, FastAPI, AWS, PostgreSQL, Async Programming, Background Job Orchestration, Distributed Systems, Retrieval-Augmented Generation (RAG), Vector Search, Embeddings, Multi-Tenant Architecture, RBAC, Audit Logging, LangGraph, Agentic Workflows, Tool-Calling, State Machines, Human-in-the-Loop Approval Patterns, LLM Cost Optimization, Prompt Engineering, Eval Design, API Design, Schema-Driven APIs, CI/CD

Plus:

Amazon Bedrock, OpenAI, Anthropic, LangChain, SSO, SAML, OIDC, Graph Data Modeling, Entity Resolution, Entity Deduplication, OpenTelemetry, Distributed Tracing, Terraform, Feature Flags, Canary Deployments

OKR''s (Objectives & Key Results)

Lead iterative development of modern, cloud-based backend systems that power multiple products and features, emphasizing business impact, reliability, and code quality.

Drive backend architecture decisions across APIs, data models, and workflows to ensure scalability, resilience, and clarity as the platform evolves.

Own problems end-to-end by breaking down ambiguity, proposing pragmatic plans, shipping iteratively, and mentoring peers through design and code reviews.

Typical Experience

7+ years of professional software development, including 5+ years building Python services in production, with strong REST API design using frameworks like FastAPI or Flask and OpenAPI-first lifecycle/versioning. Track record of owning system architecture end-to-end and driving technical decisions with minimal oversight.

Deep experience with relational databases (PostgreSQL/MySQL) including schema design, migrations, query optimization, and ensuring data correctness at scale.

Hands-on background processing/orchestration (Dagster/Celery/Airflow), containerized deployments (Docker/Kubernetes), and cloud platform usage (AWS preferred), including CI/CD, multi-tenant isolation, RBAC, and security best practices for regulated workloads.

Experience designing and productionizing LLM-driven or agentic systems — tool-calling, orchestration frameworks such as LangGraph, RAG/vector search — required, not preferred.

AI Usage in Role

This role is expected to integrate LLM and AI capabilities, including agentic workflows, into backend services as part of delivering product features. That includes managing prompts and versions, orchestrating multi-step reasoning workflows, and instrumenting AI-driven systems for cost, latency, reliability, and observability. The engineer is also expected to build in safety and operational controls such as guardrails, robust error handling, retries and timeouts, and monitoring so AI-assisted functionality behaves predictably in production.

Separately, this role is expected to use AI-assisted development tools (e.g., Claude Code, agentic IDE tooling) as a standard part of day-to-day engineering work, spec-driven and AI-paired coding, automated code review, test generation, and agent-driven refactoring.