AI/ ML Computer Vision Lead
Location
Mumbai | India
Job description
Location: Mumbai, Chennai, Bangalore
Experience: 10 to 15 years CTC: Upto 35 LPA Qualifications - Overall 10 years of industry work experience in computer vision, object detection, pattern recognition, artificial intelligence, automation, and/or vision processing.
- 5+ years of relevant experience as a CV Engineer, Data Scientist, Machine Learning Engineer, or related role.
- Experience with common languages (e.g., Python, SQL) and tools (e.g., TensorFlow, PyTorch, distributed training / inference with Spark) in the ML toolkit.
- Knowledge of CUDA, OpenCL, OpenGL, and, OpenCV
- Proficient in at least one of: PyTorch (Preferable), TensorFlow and Keras
- Coding experience in programming Languages: Python (definite), Nodejs, Javascript or, Java
- Experience with designing and developing popular - highly scalable, distributed ML models and open-source projects.
- Knowledge of text detection & OCR, human / face detection, generative models, video analytics, model compression / optimization.
- NLP techniques to process text, image processing techniques and perform entity extraction
- Very good understanding and knowledge of Statistical and ML Concepts:
- Statistics
- EDA (Univariate/ Bivariate/ Multivariate Analysis)
- Hypothesis Testing
- Regression/ Classification and Unsupervised Approaches
- Algorithms (Generic and Bayseian)
- Ensemble approaches
- Evaluation techniques
- Familiarity with various operating systems (e.g. Windows, UNIX) and databases (e.g. MySQL)
- Must have worked on MLOps Tools: MLFlow, Kubeflow, DVC etc.
- Deploying code on one of the:
- Cloud Platforms - Azure, AWS, GCP.
- Standalone Systems (Using Flask/ FastAPI/ Docker/ Kubernetes etc.)
- Handling Code with respect to various languages - PMML, Pickle, ONNX etc.
- Good team player and excellent written and verbal technical communication skills
- MS / PhD in engineering or quantitative discipline (e.g., Statistics, Mathematics, Computer Science, etc.)
Role Roles & Responsibilities - - The role is to lead the AI team by designing and developing scalable solutions using AI tools.
- - To turn business requirements into analytical questions effectively and provide meaningful recommendations.
- - Perform research and testing to develop machine learning algorithms and predictive models.
- - Solution the Data Pipeline Management (DPM) for respective Use-Cases.
- - As a Lead AI Engineer, one needs to test, tune, integrate, package and monitor solutions throughout the ML Cycle.
- - Guide the AI/ML Engineers on their daily tasks and help them solve any challenges they encounter technically.
- - Come up with post production activities to monitor the model decay, data drift and apply Retraining approaches to ensure respective KPI's are constantly met.
- - Track daily progress from a solution standpoint. Identify risks and mitigate them.
Responsibilities - - Deliver robust, well-tested, and fully documented modules to serve the use cases
- - Learn and implement state-of-the-art deep learning algorithms to support people and product association
- - Collaborate with system architects, designers, and engineers to support the development of robust machine-learning systems
- - Continuously improve the efficiency and robustness of existing modules
- - Work with Product Management to prioritize feature development
- - Work with engineering team to implement the entire application modules as discoverable microservices, experience hosting and deploying ML solutions
- - Perform code reviews and ensuring proper design and delivery
- - Promote best practices and establish team processes
- - Identify infrastructure and architectural investment needs
Skills: standalone systems,mlops,model compression,onnx,ml concepts,tensorflow,cloud platforms,vision processing,pytorch,fastapi,entity extraction,ensemble learning,deep learning,javascript,eda,regression,pattern recognition,microservices,automation,opencl,nlp,cuda,opencv,keras,statistical modeling,docker,kubernetes,pmml,pickle,mlops tools,java,hypothesis testing,data pipeline management,nodejs,classification,saas,code deployment,operating systems,flask,ocr,databases,evaluation techniques,text detection,unsupervised learning,technical communication,python,object detection,machine learning systems,model optimization,generative ai,opengl,computer vision,machine learning,artificial intelligence
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