Python Developer GenAI & Agentic AI

We are seeking a highly skilled Python Developer with strong expertise in Generative AI and Agentic AI frameworks to join our team. The ideal candidate will have hands-on experience building intelligent AI solutions, integrating large language models (LLMs), and developing autonomous AI agents capable of reasoning and task execution. Experience with Elasticsearch is desirable.

• Design, develop, and deploy AI-powered applications using Python.
• Build and implement Generative AI solutions leveraging Large Language Models (LLMs).
• Develop and orchestrate Agentic AI workflows, including autonomous agents capable of planning, reasoning, and executing tasks.
• Integrate AI models with enterprise applications and external APIs.
• Create robust prompts, retrieval pipelines, and agent architectures to solve business problems.
• Optimize AI applications for performance, scalability, and reliability.
• Collaborate with cross-functional teams to understand requirements and translate them into technical solutions.
• Implement best practices for testing, monitoring, and deployment of AI applications.
• Utilize Elasticsearch for indexing, searching, and retrieval use cases where applicable.
• Stay current with emerging trends and advancements in GenAI and Agentic AI technologies.
• Strong programming experience in Python.
• Hands-on experience with Generative AI technologies and LLM-based application development.
• Proven experience designing and implementing Agentic AI solutions using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar.
• Experience working with prompt engineering, RAG (Retrieval-Augmented Generation), and AI orchestration patterns.
• Knowledge of integrating AI services and APIs from leading model providers.
• Familiarity with vector databases and embeddings.
• Strong problem-solving skills and understanding of software engineering best practices.

Good to Have

• Experience with Elasticsearch for search and retrieval implementations.
• Exposure to cloud platforms such as AWS, Azure, or Google Cloud.
• Experience with containerization technologies such as Docker and Kubernetes.
• Understanding of MLOps practices and AI application deployment.