Gen AI Python Developer

About the Company

About the Role

Role Title - GenAI Application Developer (Python)

Experience –7 to10 Years

Role Overview

We are looking for an experienced GenAI Application Developer (Python) to design, develop, and deploy enterprise-grade Generative AI applications and services. The ideal candidate will have strong expertise in Python-based backend development, GenAI frameworks, Retrieval Augmented Generation (RAG), vector databases, cloud-native architectures, and microservices deployment on AWS. This role requires hands-on experience integrating Large Language Models (LLMs) into business applications, building scalable APIs, and developing secure, high-performance AI solutions that support enterprise use cases such as intelligent assistants, document processing, knowledge management, search, summarization, and conversational AI.

Responsibilities

• Generative AI Application Development
• Design and develop enterprise-grade GenAI applications using Python.
• Integrate Large Language Models (LLMs) through platforms such as OpenAI, AWS Bedrock, Azure OpenAI, or Google Vertex AI.
• Develop conversational AI, document intelligence, summarization, knowledge search, and Q&A solutions.
• Implement prompt engineering strategies and optimize LLM interactions.
• Backend & API Development
• Build scalable RESTful and asynchronous APIs using FastAPI and Python.
• Design microservices architectures for AI-powered applications.
• Develop reusable APIs and services that support enterprise integrations.
• Ensure application scalability, security, performance, and reliability.
• Retrieval Augmented Generation (RAG)
• Design and implement RAG-based solutions using enterprise knowledge repositories.
• Develop document ingestion, chunking, embedding, indexing, and retrieval pipelines.
• Integrate vector databases and semantic search capabilities.
• Optimize retrieval accuracy and response quality.
• Cloud & Deployment
• Containerize applications using Docker.
• Deploy and manage applications on AWS environments using ECS, EKS, Kubernetes, or serverless services.
• Implement CI/CD pipelines and DevOps best practices.
• Monitor and optimize cloud-based AI workloads.
• Data Integration & Persistence
• Design data access layers using SQL and NoSQL databases.
• Integrate enterprise applications, APIs, and data sources.
• Implement secure storage and retrieval mechanisms for application data and embeddings.
• Ensure data reliability, consistency, and compliance with enterprise standards.
• Collaboration & Technical Leadership
• Partner with architects, product owners, data engineers, and business teams to deliver AI solutions.
• Participate in design reviews and architectural discussions.
• Contribute to coding standards, documentation, and development best practices.
• Mentor junior developers and support knowledge sharing initiatives.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field.
• 7-10 years of software development experience with strong expertise in Python.
• Proven experience building scalable backend applications using FastAPI.
• Experience integrating GenAI platforms such as:
• OpenAI
• Azure OpenAI
• AWS Bedrock
• Google Vertex AI
• Strong understanding of Large Language Models (LLMs) and prompt engineering.
• Experience developing Retrieval Augmented Generation (RAG) applications.
• Hands-on experience with vector databases such as:
• Pinecone
• OpenSearch
• Weaviate
• Chroma
• FAISS
• Experience deploying containerized applications using Docker and Kubernetes.
• Hands-on experience with AWS services and cloud-native development.
• Strong experience with both relational and NoSQL databases.

Required Skills

• Technical Skills
• Programming Languages
• Python
• SQL
• GenAI & LLM Technologies
• OpenAI
• Azure OpenAI
• AWS Bedrock
• Vertex AI
• Prompt Engineering
• AI Agents
• Frameworks
• FastAPI
• LangChain
• LangGraph
• LlamaIndex
• RAG & Vector Databases
• Pinecone
• OpenSearch
• Chroma
• Weaviate
• FAISS
• Cloud & DevOps
• AWS
• ECS
• EKS
• Kubernetes
• Docker
• CI/CD Pipelines
• Databases
• PostgreSQL