Junior AI Engineer - Generative AI & Cloud_96666

Key Skills: LLM, Python, Generative AI, AWS, Azure

Roles and Responsibilities:

Generative AI Development

* Build and integrate applications using Large Language Models (LLMs).

* Develop RAG (Retrieval-Augmented Generation) pipelines.

* Implement prompt engineering and structured output generation.

* Integrate AI services via APIs (OpenAI, Azure OpenAI, AWS Bedrock, etc.).

* Build AI-powered chatbots, assistants, and internal productivity tools.

Python Engineering

* Write clean, scalable Python code for AI workflows.

* Develop REST APIs using FastAPI or Flask.

* Work with JSON, structured data, embeddings, and vector stores.

* Build data processing scripts for AI pipelines.

Cloud & Deployment

* Deploy AI applications using:

o AWS (S3, Lambda, Bedrock, EC2, API Gateway) OR

o Azure (Azure OpenAI, Functions, Blob Storage, App Services)

* Containerize applications using Docker (basic level).

* Support CI/CD pipelines and cloud deployments.

Data & Integration

* Work with structured and unstructured data sources.

* Build connectors to databases (PostgreSQL, MySQL, SQL Server).

* Assist in creating vector databases (FAISS, Pinecone, OpenSearch, etc.).

* Support model evaluation and logging.

Skills Required:

• 1-3 years of experience in software engineering or AI development
• Strong proficiency in Python.
• Experience working with APIs and JSON-based integrations.
• Basic understanding of:
• o LLMs and Generative AI
• o Prompt engineering
• o Embeddings & vector search
• Hands-on experience in AWS or Azure.
• Familiarity with Git and collaborative development workflows.
• Experience with:
• o LangChain, LlamaIndex, or similar frameworks
• o Azure OpenAI or AWS Bedrock
• o FastAPI
• o Vector databases
• Basic understanding of:
• o RAG architecture
• o AI model evaluation
• o MLOps fundamentals
• Exposure to Databricks is a plus.

• Python programming for AI/ML workflows
• LLM integration and prompt engineering
• RAG pipeline development
• REST API development (FastAPI/Flask)
• Cloud platforms: AWS or Azure
• Working with embeddings and vector databases
• API integrations and JSON data handling
• Docker basics and cloud deployment
• Git version control and collaborative development
• Basic understanding of MLOps and AI model evaluation

Education: Bachelor's Degree in related field