• Design, develop, train, evaluate, and optimize machine learning and deep learning models.
• Perform feature engineering, data preparation, and dataset validation to improve model performance.
• Translate business and technical requirements into measurable machine learning objectives and success metrics.
• Conduct experiments, benchmarking, and model evaluations to validate and improve ML solutions.
• Develop modular and reusable Python pipelines for data preprocessing, feature extraction, model training, and inference.
• Deploy machine learning models as scalable APIs or microservices with proper versioning and experiment tracking.
• Build and maintain automated training and inference workflows while monitoring model accuracy, latency, and drift in production environments.
• Optimize model inference and data pipelines for performance using CPU/GPU optimization techniques such as batching,mixed precision (FP16), and parallel processing.
• Containerize machine learning applications and implement CI/CD pipelines to support reliable deployments.
• Design and maintain ETL pipelines for processing structured and unstructured data, including image, audio, and video datasets.
• Implement logging, monitoring, and alerting to ensure the reliability, scalability, and continuous improvement of ML services.
• Collaborate

Requirements

• Minimum 2 years of experience in AI, Machine Learning, or Deep Learning development.
• Strong knowledge of supervised and unsupervised learning, deep learning, and transfer learning techniques.
• Experience working with CNNs, RNNs, Transformers, computer vision, and multimodal AI models.
• Proficiency in Python programming and Bash scripting.
• Experience developing REST APIs using FastAPI or Flask.
• Hands-on experience with PyTorch, TensorFlow, Scikit-learn, NumPy, Pandas,OpenCV, Librosa, Hugging Face, OpenCLIP, and TorchVision.
• Familiarity with Docker, GitHub, CI/CD pipelines, MLflow, Weights & Biases, ONNX, and TorchScript.
• Working knowledge of Linux environments, SSH, GPU utilization, multiprocessing, and basic AWS cloud services.
• Experience designing scalable machine learning systems and deploying production-ready ML microservices.
• Strong debugging, troubleshooting, and performance optimization skills.
• Ability to write clean, maintainable, and scalable code following software engineering best practices.
• Strong analytical, problem-solving, and experimentation skills.
• Excellent technical documentation, communication, and collaboration skills.
• Understanding of Agentic AI concepts, including AI agents, workflow orchestration,tool integration, and autonomous task execution using modern LLMframeworks.
• Experience working directly with clients to understand business requirements and translate them into practical, scalable AI/ML solutions.