Key Skills Required

Core Engineering
Strong Python; TypeScript / Node.js a plus
REST / gRPC API design, async programming, FastAPI or equivalent
Experience building cloud-native microservices
Hands-on experience with Kafka-based event-driven architecture
Practical understanding of Domain-Driven Design (DDD) concepts
Git, GitHub Actions, FluxCD, GitOps workflows
Docker, Kubernetes, Helm
GenAI / LLM Development
Hands-on with LLM APIs (OpenAI, Anthropic, Azure OpenAI, open-source models)
Prompt engineering, prompt versioning, structured output (JSON schema, Pydantic)
RAG pipeline implementation — chunking, embeddings, vector stores (pgvector, FAISS, Pinecone, Weaviate)
Function calling, tool use, and agent loops
Agentic Frameworks & Tooling
LangChain, LlamaIndex, LangGraph, AutoGen, CrewAI, or equivalent
MCP server/client implementation
Familiarity with Speckit, GitHub Copilot extensions, Figma MCP integration




Experience building coding agents, review agents, or test-generation agents
Evaluation & Guardrails
Working knowledge of eval frameworks (Ragas, DeepEval, promptfoo, LangSmith)
Guardrail implementation (NeMo Guardrails, Guardrails AI, custom validators)
Observability: LangFuse, OpenTelemetry, token/cost dashboards
Data & Integration
SQL and NoSQL basics; data pipelines for ingestion and indexing
Integration with enterprise systems (Jira, Confluence, GitHub, Artifactory)
Cloud services (Azure / AWS / GCP) — storage, compute, identity
Soft Skills
Collaborative team player; works closely with the Solution Architect and AI/ML Engineer
Quick learner who stays current with the rapid-moving GenAI ecosystem
Strong ownership — ships production-grade code, not prototypes.

📌 AES - DE - Generative AI Application Developers (Pune)
🏢 Zensar Technologies
📍 Pune