Lead Software Engineer

Summary

Engagement : Contractor, hands-on IC-lead (70–80% coding; 20–30% technical leadership).

Experience : 8–12 years total; 3–5 years leading squads / streams while remaining deep in code.

Stack : Java, Spring Boot, REST APIs, MySQL / PostgreSQL and MongoDB / Cassandra, Docker / Kubernetes, Git, OAuth2 / JWT, one cloud (AWS / GCP / Azure).

GenAI / AI Tools : Proficient with AI coding copilots and code-generation tooling; experience designing and integrating GenAI services; comfortable with “vibe coding” workflows.

Outcome : Ship secure, scalable services quickly; mentor engineers informally; drive design, reliability, CI / CD, and AI-enabled productivity.

Required Qualifications

8–12 years backend / platform engineering with strong, recent hands-on in Java / Spring Boot.

Designed, built, and scaled RESTful APIs in production; API versioning, schema evolution, and backward compatibility.

Data depth across MySQL / PostgreSQL and MongoDB / Cassandra; indexing, optimization, and caching patterns.

Implemented RBAC, OAuth2 / OIDC, JWT in real-world systems.

Proficient with Git workflows (trunk-based or GitFlow), code reviews, and branching strategies.

Testing : unit, integration, contract (e.g., Pact), and performance tooling; API tooling with Swagger / OpenAPI and Postman.

Production-grade Docker and Kubernetes experience (deployments, services, ingress, HPA, rollout / rollback).

Cloud proficiency on at least one of AWS / GCP / Azure (compute, networking, storage, IAM, managed DBs).

AI Coding Tools and GenAI (new)

Daily-use proficiency with at least one AI coding tool in IDE / CLI; able to set team guardrails, policies, and best practices.

Hands-on experience integrating at least one LLM platform (e.g., Azure OpenAI, AWS Bedrock, Vertex AI, or OpenAI / Anthropic APIs) into backend services.

Practical prompt engineering and evaluation; experience with RAG using vector stores (e.g., OpenSearch, pgvector, Pinecone, Redis) and embeddings.

Familiarity with model lifecycle concerns : versioning, offline / online evaluation, A / B testing, telemetry, safety / abuse handling.

Comfort with “vibe coding” : collaborative AI-assisted workflows, rapid prototyping, live coding with AI agents, and converting exploratory prompts into production-grade code and tests.

Clear communicator; able to ramp quickly and deliver independently with minimal supervision.