AI/Ml programmer

Job description The JD for AI/Ml programmer is given below: Key Responsibilities: Design, develop, and deploy Generative AI models using state-of-the-art architectures (e.g., Transformers, Diffusion models). Build and fine-tune LLM-powered agents capable of multi-step reasoning, task planning, and tool use. Work with frameworks like LangChain, AutoGPT, BabyAGI, CrewAI, or similar agent orchestration tools. Integrate models with REST APIs, vector databases (e.g., Pinecone, FAISS, Chroma), and external systems. Optimize inference pipelines for performance, latency, and scalability. Collaborate with product managers and data scientists to prototype and productionize AI features. Stay updated on recent advancements in Generative AI and autonomous agents. Required Qualifications: 34 years of hands-on experience in Machine Learning / Deep Learning, with at least 1–2 years in Generative AI and/or AI Agents. Proficiency in Python and ML libraries such as PyTorch, TensorFlow, Transformers (Hugging Face). Experience with LLM APIs (OpenAI, Claude, Mistral, etc.) and building LLM-based applications. Solid understanding of prompt engineering, fine-tuning, RAG (Retrieval-Augmented Generation), and multi-modal learning. Familiarity with agent orchestration frameworks and LLM tool chaining. Strong problem-solving and communication skills. Preferred Qualifications: Experience with cloud platforms (AWS, GCP, Azure) and MLOps tools (MLflow, Weights & Biases). Knowledge of Reinforcement Learning or Meta-learning for agent training. Experience contributing to open-source projects or published papers in the field of AI. Role: Machine Learning Engineer Industry Type: IT Services & Consulting Department: Data Science & Analytics Employment Type: Full Time, Permanent Role Category: Data Science & Machine Learning Education UG: B.Tech/B.E. in Any Specialization PG: Any Postgraduate Key Skills Skills highlighted with ‘‘ are preferred keyskills Deep LearningMachine Learning Algorithms Prompt Engineeringfine-tuningRetrieval-Augmented GenerationMl AlgorithmsAi AlgorithmsPyTorchLLM APIsagent orchestration frameworksTransformersRAGmulti-modal learningTensorFlow