• US Citizen

• Green Card

• EAD (OPT/CPT/GC/H4)

• H1B Work Permit

• Corp-Corp

• W2-Permanent

• W2-Contract

• Contract to Hire

• Consulting/Contract

• UG :- - Not Required

• PG :- - Not Required

• No of position :- ( 1 )

• Post :- 20th Dec 2024

•             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.