smartData - Lead Python Developer - LLM / Generative AI

Job Summary :

We are seeking a highly skilled and innovative Python Lead Developer with strong expertise in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Machine Learning, Deep Learning, and Computer Vision.

The ideal candidate will lead AI-driven solution development, mentor team members, and collaborate with stakeholders to build scalable, intelligent, and high-performance Responsibilities :

• Lead design, development, and deployment of AI / ML solutions leveraging LLMs, RAG pipelines, and generative AI frameworks.
• Architect and implement end-to-end machine learning workflows, including data pre-processing, training, fine-tuning, evaluation, and deployment.
• Build scalable applications using Python frameworks and integrate AI models into production systems.
• Apply computer vision and deep learning techniques for image / video analytics, object detection, OCR, etc.
• Optimize model performance, inference speed, and cost efficiency.
• Stay updated with the latest advancements in NLP, LLMs, transformers, and multimodal AI.
• Collaborate with cross-functional teams (Data Engineers, DevOps, Product Owners) for solution delivery.
• Mentor and guide junior developers in AI / ML best practices and coding Skills & Experience :
• 7- 13 years of professional experience in software development with at least 5+ years in Python.
• Proven expertise in Generative AI, LLMs (OpenAI, Hugging Face, LangChain, etc.), and RAG frameworks.
• Strong background in Machine Learning, Deep Learning, and Computer Vision (CNNs, YOLO, OpenCV, TensorFlow, PyTorch).
• Hands-on experience with NLP, embeddings, fine-tuning, and vector databases (Pinecone, FAISS, Weaviate, etc.
• Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps practices.
• Solid understanding of software architecture, design patterns, and microservices.
• Excellent problem-solving, analytical, and communication to Have :
• Experience with multimodal AI (text, image, video, audio).
• Exposure to reinforcement learning (RLHF).
• Knowledge of Kubernetes / Docker for AI deployment.
• Contribution to open-source AI / ML projects.

(ref : hirist.tech)