AES - DE - Generative AI Application Developers

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.

Part of Agentic AI team

BE Computer, 4-8 years of experience