Zensar Technologies—AES - DE - Generative AI Application Developers

Job description

Description: 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 fast-moving GenAI ecosystem - Strong ownership — ships production-grade code, not prototypes.