Junior AI Engineer - Generative AI & Cloud

About the job

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 chat bots, 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: • LLMs and Generative AI • Prompt engineering • Embeddings & vector search • Hands-on experience in AWS or Azure. • Familiarity with Git and collaborative development workflows. • Experience with: • LangChain, LlamaIndex, or similar frameworks • Azure OpenAI or AWS Bedrock • FastAPI • Vector databases • Basic understanding of: • RAG architecture • AI model evaluation • 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

Requirements

• LLM
• Python
• Generative AI
• AWS
• Azure

Qualifications

• Bachelor's Degree in related field

Preferred Technologies

• LLM
• Python
• Generative AI
• AWS
• Azure