Lead software engineer

18 hours ago

Bengaluru, India Grid Dynamics Full time

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, My SQL/Postgre SQL and Mongo DB/Cassandra, Docker/Kubernetes, Git, OAuth2/JWT, one cloud (AWS/GCP/Azure).- Gen AI/AI Tools: Proficient with AI coding copilots and code-generation tooling; experience designing and integrating Gen AI 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 My SQL/Postgre SQL and Mongo DB/Cassandra; indexing, optimization, and caching patterns.- Implemented RBAC, OAuth2/OIDC, JWT in real-world systems.- Proficient with Git workflows (trunk-based or Git Flow), code reviews, and branching strategies.- Testing: unit, integration, contract (e.g., Pact), and performance tooling; API tooling with Swagger/Open API 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 Gen AI (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 Open AI, AWS Bedrock, Vertex AI, or Open AI/Anthropic APIs) into backend services.- Practical prompt engineering and evaluation; experience with RAG using vector stores (e.g., Open Search, 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.

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