Sr. 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: AI/ML Frameworks: OpenAI GPT-4 Vision models, Fine-tuning open-source models, RAG (Retrieval Augmented Generation) Computer Vision Libraries: Open CV, DLIB, SimpleCV Cloud Platforms: Azure, AWS Data and Workflow Tools: Databricks, Apache Airflow Programming Languages: Python, SQL, C++ Model Deployment & Optimization: ONNX, TensorRT, Docker, Kubernates, Fast API Other Tools: Model performance evaluation frameworks, logging and monitoring toolsOther Soft Skills: Strong communication and leadership abilities, effectively working with technical and non-technical stakeholders. Capable of owning projects end-to-end while balancing multiple priorities and ensuring timely delivery.