AI ML Computer Vision Lead - Mumbai, India - YO HR CONSULTANCY

Description

Location: Mumbai ChennaiBangaloreExperience:10 to 15yearsCTC: Upto35LPA
Qualifications:Overall 10 years of industry work experience in computer visionobject detection pattern recognition artificial intelligenceautomation and/or vision processing. 5 yearsof relevant experience as a CV Engineer Data Scientist MachineLearning Engineer or related role.Experience with common languages (e.g. Python SQL) and tools (e.g.TensorFlow PyTorch distributed training / inference with Spark) inthe ML toolkit. Knowledge of CUDA OpenCLOpenGL and OpenCV Proficient in at least oneof: PyTorch (Preferable) TensorFlow andKeras Coding experience in programmingLanguages: Python (definite) Nodejs Javascript orJava Experience with designing anddeveloping popular highly scalable distributed ML models andopensource projects. Knowledge of textdetection & OCR human / face detection generative modelsvideo analytics model compression /optimization. NLP techniques to process textimage processing techniques and perform entityextraction Very good understanding andknowledge of Statistical and ML Concepts:Statistics EDA (Univariate/ Bivariate/Multivariate Analysis) HypothesisTesting Regression/ Classification andUnsupervised Approaches Algorithms (Genericand Bayseian) Ensembleapproaches Evaluationtechniques Familiarity with variousoperating systems (e.g. Windows UNIX) and databases (e.g.MySQL) Must have worked on MLOps Tools:MLFlow Kubeflow DVC etc. Deploying code onone of the: Cloud Platforms Azure AWSGCP. Standalone Systems (Using Flask/FastAPI/ Docker/ Kubernetes etc.) HandlingCode with respect to various languages PMML Pickle ONNXetc. Good team player and excellent writtenand verbal technical communication skills MS/ PhD in engineering or quantitative discipline (e.g. StatisticsMathematics Computer Scienceetc.)

Roles&ResponsibilitiesRole:

• The role is to lead the AI team by designing and developingscalable solutions using AI tools.
• To turnbusiness requirements into analytical questions effectively andprovide meaningful recommendations.
• Performresearch and testing to develop machine learning algorithms andpredictive models.
• Solution the Data PipelineManagement (DPM) for respective UseCases.
• As aLead AI Engineer one needs to test tune integrate package andmonitor solutions throughout the ML Cycle.
• Guide the AI/ML Engineers on their daily tasks and help them solveany challenges they encounter technically.
• Come up with post production activities to monitor the model decaydata drift and apply Retraining approaches to ensure respective KPIs are constantly met.
• Track daily progressfrom a solution standpoint. Identify risks and mitigatethem.

Responsibilities:

• Deliver robust welltested and fully documented modules to serve theuse cases
• Learn and implement stateoftheartdeep learning algorithms to support people and productassociation
• Collaborate with system architectsdesigners and engineers to support the development of robustmachinelearning systems
• Continuously improvethe efficiency and robustness of existingmodules
• Work with Product Management toprioritize feature development
• Work withengineering team to implement the entire application modules asdiscoverable microservices experience hosting and deploying MLsolutions
• Perform code reviews and ensuringproper design and delivery
• Promote bestpractices and establish team processes
• Identify infrastructure and architectural investmentneeds

datapipeline management,unsupervised learning,ocr,aws,kubeflow,modelcompression,distributed training,mlops tools,kubernetes,objectdetection,dvc,databases,statistical modeling,python,machinelearning,nodejs,ml concepts,tensorflow,docker,ensemblelearning,deep learning,saas,video analytics,algorithms,modeloptimization,computer vision,statistical concepts,mlflow,facedetection,mlops,ensemble approaches,artificialintelligence,technical communication,javascript,pmml,operatingsystems,text detection,microservices,generative ai,pickle,visionprocessing,gcp,sql,java,keras,evaluation techniques,pytorch,cloudplatforms,cuda,flask,machine learningsystems,nlp,automation,opencv,code deployment,statistics,generativemodels,opencl,classification,fastapi,standalonesystems,unsupervised approaches,eda,regression,hypothesistesting,opengl,azure,entity extraction,onnx,patternrecognition