Educational Requirements

- Bachelor of Engineering

Responsibilities

- GenAI / LLM Engineering: Build LLM-powered applications (chatbots, copilots, summarization, knowledge assistants) using OpenAI/Azure OpenAI/Anthropic/Gemini or open-source LLMs.

- Implement RAG pipelines: data ingestion, chunking, embeddings, vector search, prompt assembly, response generation.

- Improve response quality using prompt engineering, retrieval tuning (hybrid search, metadata filters), and basic RAG evaluation practices.

- ML Engineering (non-platform): Develop and deploy ML components (classification, NLP, forecasting) using scikit-learn / PyTorch / TensorFlow as needed.

- Package AI/LLM solutions into production-grade services using FastAPI/Flask.

- Write clean, reusable Python modules and follow engineering best practices (testing, logging, code quality).

- Deployment Operations (LLMOps exposure): Support deployment to cloud environments: AWS (SageMaker/ECS/Lambda) or Azure (Azure ML/AKS/App Services).

- Implement basic observability: logs, error handling, latency tracking, token usage tracking (where applicable).

- Assist in quality, safety, and governance practices: PII redaction, content filtering, prompt-injection mitigation, secure access controls.





Additional Responsibilities

- Vector databases: Pinecone / Qdrant / Chroma / Weaviate / FAISS.

- Frameworks: LangChain / LangGraph / LlamaIndex / Semantic Kernel.

- Evaluation tools: RAGAS / TruLens / DeepEval, prompt testing frameworks.

- Containerization: Docker (Kubernetes is optional).

- CI/CD exposure: GitHub Actions / Azure DevOps / Jenkins.

- Data pipelines: Airflow / Prefect / Databricks.

- Safety tooling: Presidio, content safety filters, access control patterns.

Technical and Professional Requirements

- Python programming (robust fundamentals, OOP, writing APIs, debugging).

- Hands-on experience building GenAI/LLM solutions: RAG / embeddings / vector DB / prompt engineering.

- Experience with FastAPI or Flask (building and serving APIs).

- Understanding of LLM application lifecycle (prompting, evaluation, versioning, deployment basics).

- Knowledge of at least one cloud platform: AWS or Azure.

- Basic understanding of Git, code reviews, and deployment workflows.

Preferred Skills

- Technology- AI-Generative AI- Artificial Intelligence - BASIC

- Technology- AI-Generative AI- Generative AI - Basic

📌 Junior AI Engineer (Bengaluru)
🏢 Infosys
📍 Bengaluru