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

Description

Location:
Mumbai ChennaiBangalore

Experience:10 to 15years


CTC:

Upto35LPAQualifications:
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&Responsibilities


Role:
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 casesLearn and implement stateoftheartdeep learning algorithms to support people and productassociationCollaborate with system architectsdesigners and engineers to support the development of robustmachinelearning systemsContinuously improvethe efficiency and robustness of existingmodulesWork with Product Management toprioritize feature developmentWork withengineering team to implement the entire application modules asdiscoverable microservices experience hosting and deploying MLsolutionsPerform code reviews and ensuringproper design and deliveryPromote bestpractices and establish team processesIdentify 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