Job Description:

- We are looking for a Python AI Engineer 2 3 yrs who can build and productionize AI GenAI solutions especially LLM powered applications such as RAG systems summarization classification and agentic workflows
- The role is engineering led strong Python coding API development deployment readiness and basic operational practices monitoring evaluation guardrails
- This is not an ML platform role

Key Responsibilities:

- Key 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

Technical 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

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

Preferred Skills:

Technology->AI-Generative AI->Generative AI - Basic,Technology->AI-Generative AI->Artificial Intelligence - BASIC

📌 Junior AI Engineer (India)
🏢 Infosys
📍 India