Junior AI Engineer

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 strong 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