Lead ML Engineer

Requirements:

• Bachelors or Masters degree in Computer Science Data Science Statistics or a related field.
• 4 to 6 years of hands-on experience in machine learning model development including NLP NER and large language models.
• Strong proficiency in Python and experience with machine learning frameworks such as TensorFlow PyTorch and Scikit-learn.
• Proven experience in developing models using BERT LangChain RAG and related modern AI frameworks.
• Strong understanding of neural networks deep learning concepts and their underlying mathematical principles.
• Experience deploying machine learning models into production environments.
• Experience developing APIs and working with backend frameworks such as FastAPI Django or Flask.
• Experience with Docker and implementing CI/CD pipelines.
• Experience building and managing MLOps pipelines and using version control tools such as DVC.
• Familiarity with image processing and computer vision techniques.
• Experience with auto-scaling systems and cloud-based deployments.
• Experience working with GANs Stable Diffusion or other generative models.
• Experience with 3D neural networks and meta-learning techniques.
• Experience deploying machine learning models on embedded systems such as Raspberry Pi.
  

Responsibilities:

• Design develop and implement advanced machine learning and deep learning models with a focus on natural language processing (NLP) named entity recognition (NER) and large language models (LLMs) such as BERT.
• Build AI agents and multi-agent workflows using frameworks such as LangChain LlamaIndex and AutoGen and develop Retrieval Augmented Generation (RAG) applications.
• Develop and optimize neural network architectures to ensure models are efficient scalable and production-ready.
• Integrate multimodal capabilities by incorporating image processing techniques including image segmentation object detection and facial recognition.
• Train fine-tune and optimize large language models using techniques such as LoRA QLoRA and quantization.
• Develop and deploy machine learning models into production environments ensuring reliability and performance.
  Build custom APIs using frameworks such as FastAPI Django or Flask to support AI and ML applications.
• Containerize applications using Docker and implement CI/CD pipelines to enable efficient deployment and updates.
• Establish and manage MLOps pipelines using tools such as Data Version Control (DVC) to ensure proper versioning and reproducibility.
• Conduct research to stay current with emerging AI machine learning and deep learning trends and technologies.
• Document methodologies models and technical processes and prepare reports and presentations for technical and non-technical stakeholders.
• Collaborate with cross-functional teams to integrate AI solutions into the organizations products and systems.