Senior Computer Vision ML Engineer

Looking for a Senior Computer Vision ML Engineer with over 8 years of experience specializing in building and deploying scalable AI / ML systems based on image recognition and computer vision. Proven track record of leading projects from conception to deployment, collaborating with cross-functional teams, and implementing high-performance machine learning pipelines. Expertise in building Gen AI products, Computer vision, LLM systems, and search-driven applications. Skilled in leveraging advanced technologies like vision models & GPT4 models, hybrid search techniques, and scalable infrastructure across cloud platforms such as Azure

Responsibilities-

• Building computer vision algorithms for tasks such as object detection, image segmentation, pose estimation and scene understanding.
• 5 years of experience in computer vision and machine learning including deploying models in production.
• Fine-tuning open-source models and deploying them for real-world applications.
• Experience with OpenAI models (e.g., GPT-4) vision models.
• Expertise in implementing hybrid search and retrieval-augmented generation (RAG) techniques.
• Advanced testing and evaluation of different chunking strategies for optimized performance.
• Experience with computer libraries such as OpenCV, DLIB or similar.
• Proficient in designing and implementing deep learning architectures (e.g, CNNs, RNNs, transformers).
• Experience with Azure cloud platforms and containerization tools like Docker and Kubernetes.
• Familiarity with ML lifecycle tools (e.g., MLflow, DVC).
• Deep knowledge of Azure AI studio.
• Understanding LLM, RAG and Gen AI concepts.
• Hands-on experience with building and managing large-scale machine learning systems.
• Deep knowledge of infrastructure-side challenges, such as scaling models, load testing, and ensuring high availability.
• Strong focus on performance optimization, continuous integration, and improving ML systems for deployment at scale.
• Extensive experience leading machine learning projects end-to-end, from design and development to deployment and monitoring.
• Collaborates closely with stakeholders, ML engineers, data scientists, and DevOps teams to ensure successful project delivery.
• Builds out evaluation frameworks that incorporate user feedback from logging and fine-tuning model performance accordingly.
• Building robust data pipelines for machine learning models, ensuring that data is clean, properly preprocessed, and available for model training and deployment.
• Expertise in automating ML pipelines using Airflow and optimizing workflows in distributed environments.
• Experience in integrating and managing large datasets for training complex models, including deep learning frameworks.

Technologies and Tools :