Job Responsibilities :

- 5 - 10 years overall experience in software engineering

- Hands-on experience working as Sr AI/ML Developer with at least 1 complete production grade agentic AI system implementation

- Hands-on experience with cloud AI services (Azure OpenAI, AWS Bedrock, Google Vertex AI)

- Hands-on experience with Python AI/ML frameworks (LangChain, LlamaIndex, Transformers, PyTorch)

- Hands-on experience integrating LLMs with external systems and enterprise applications

- Experience working with vector databases, knowledge graphs, and RAG pipeline development

- Advising on best practices for AI agent development and enterprise AI integration processes

- Experience in deploying AI models and agents in multiple environments (dev, staging, production)

- Experience managing stakeholder communication regarding AI capabilities, limitations, and project timelines

What we're looking for :

- Proficient in developing, deploying, and orchestrating multi-agent AI systems

- Demonstrated proficiency in LLM fine-tuning, prompt engineering, and model optimization

- Demonstrated proficiency in designing and implementing RAG (Retrieval-Augmented Generation) systems

- Demonstrated proficiency in using AI frameworks like LangChain, LlamaIndex, and Hugging Face ecosystem

- Demonstrated proficiency with Python development and AI/ML libraries

- Demonstrated proficiency in JavaScript/TypeScript for AI frontend integration

- Familiarity with MLOps tools and practices

- Experience in building conversational AI interfaces, chatbots, and AI-powered applications

- Experience with API development for AI services and webhook management

- Experience in AI agent orchestration, planning algorithms, and decision-making frameworks

- Must have college degree in Computer Science, AI/ML, or equivalent experience

Tech Stack :

- Python, FastAPI, Flask

- LangChain, LlamaIndex, Transformers library

- OpenAI API, Azure OpenAI, Anthropic Claude

- Vector databases (Pinecone, Weaviate, ChromaDB)

- Git, Docker, Kubernetes

- JavaScript/TypeScript for AI integration

- Postman for API testing

- SQL/NoSQL databases for AI data management

- AI model performance monitoring and optimization

- Prompt engineering and AI safety practices