Python AI/ML - GenAI Professional

Job highlights

• 5+ years Python development with ML/DL expertise, skilled in TensorFlow, PyTorch, GenAI tools (Vector DB, Langchain, RAG), and model deployment
• Design, develop, and deploy AI/ML models and GenAI applications; integrate APIs and data sources; mentor team members and collaborate on AI features

Job Summary

This role is ideal for candidates passionate about solving complex problems, driving innovation, and implementing next-gen AI solutions for real-world applications.

Responsibilities

• Design, develop, and deploy robust solutions leveraging Core Python , standard libraries, and frameworks.
• Build and optimize end-to-end machine learning and deep learning models for production use.
• Architect and implement GenAI-powered applications utilizing Vector Databases, Langchain, Langgraph, RAG, and MCP.
• Integrate external data sources and APIs, and build scalable pipelines for AI model deployment.
• Collaborate with internal teams to translate business requirements into cutting-edge AI features.
• Conduct model evaluations, fine-tuning, and support ongoing model monitoring.
• Mentor and guide team members and contribute to best practices within the AI community.

Qualifications Required

• Minimum 5 years of professional experience in Python development, with a focus on machine learning and deep learning.
• Strong understanding of core ML concepts , with proven project delivery using frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Hands-on experience in deep learning (CNNs, RNNs, LLMs/Transformers, or similar architectures).
• Demonstrable expertise in GenAI tools (Vector Databases, Langchain, Langgraph, Retrieval Augmented Generation (RAG), MCP).
• Introductory knowledge of AI Agents and their integration in larger workflows.
• Solid understanding of model deployment, API integration, version control, and containerization.
• Excellent communication, problem-solving skills, and a collaborative mindset.

Preferred Qualifications

• Experience deploying AI/ML solutions in production at scale.
• Familiarity with cloud AI services (AWS Sagemaker, Azure ML, GCP, etc.).
• Exposure to LLM fine-tuning, prompt engineering, or knowledge of autonomous agent systems.