Senior ML/GenAI Engineer Offline

Our department focuses on developing cutting-edge machine learning and artificial intelligence solutions to address complex real-world challenges. We specialize in designing, deploying, and optimizing scalable ML models, with a strong emphasis on Computer Vision, Deep Learning, and MLOps. The team collaborates with cross-functional experts, including data scientists, engineers, and product managers, to deliver high-impact AI-driven solutions for various industries. By leveraging state-of-the-art technologies and cloud platforms, we ensure the seamless integration of ML models into production environments, driving innovation and business value.

Requirements

– Master’s or Ph.D. in Data Science, Computer Science, Statistics, Applied Mathematics, or a related field.
– 5+ years of experience in designing and implementing machine learning systems in a production environment.
– Experience with Deep Learning frameworks and libraries (TensorFlow, PyTorch).
– Experience in fine-tuning Computer Vision models (YOLO, SSD, Visual Transformers)
– Proficient in programming with Python.
– Strong understanding of data structures, algorithms, and software engineering principles.
– Experience with at least one cloud platform (AWS, Azure, Google Cloud) and understanding of how to leverage their ML services.
– Knowledge of containerization and orchestration technologies (e.g., Docker, Kubernetes) for deploying ML models.
– Familiarity with MLOps principles and tools to streamline the ML lifecycle from development to production.
– Excellent communication and leadership skills, with the ability to work in a fast-paced, collaborative environment.
– Commercial experience in object detection, segmentation, and classification
– Commercial or Academic experience with the Segment Anything Model (SAM)
 

Job responsibilities

– Design and implement scalable machine learning models and algorithms that can process large volumes of data.
– Lead the development of our ML infrastructure, ensuring it supports both current needs and future growth.
– Support presale PoCs and project/account-starting activities
– Architect and optimize data pipelines for training and deploying machine learning models.
– Collaborate with data engineers and software developers to integrate ML models into production systems.
– Stay aware of new developments in machine learning, AI, and data science, and assess their applicability to our business needs.
– Work with product teams to translate business requirements into technical specifications and ML solutions.