
AIML Lead Developer
5 days ago
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 applications.
Key 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 preprocessing, 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 standards.
Required Skills & Experience:
- 713 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 skills.
Good 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.
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