Machine Learning Ops Architect_13405 {All 5 location} Full-Time

1 week ago

Bengaluru, India HyreFox Consultants Full time
Requirement (8+ Years of experience) Degree with strong analytical focus (Computer Science, Mathematics, Engineering, Statistics etc. ) Hands-on expertise in end-to-end implementation of ML projects i.e. Data collection, Model building, and Model deployment in a Production environment using MLOps ( continuous integration, continuous delivery, and continuous training) Knowledge on supervised and unsupervised algorithms for Regression, Classification, and Clustering for qualitative and quantitative data along with their trade-offs. 4-6 years of experience with Python, Python frameworks viz. Flask, Django, and web Scraping /or PySpark and Databricks. Working knowledge of Tensor Flow, Keras, Jupyter or equivalent. Experience with ML lifecycle tools: MLflow, Comet, Neptune or equivalent. Experience integrating with backend services, third-party APIs, etc. Proficient with CI/CD, Docker/Kubernetes and ML workloads. Proficient in SQL. Great problem solving and analytical skills combined with the ability to explain concepts to both technical and non-technical audiences An eagerness to learn new things and help others do the same. Familiarity with agile software delivery. Exploratory Data Analysis, Feature Extraction, Feature Selection, Hyperparameter tuning, Model evaluation( training, testing) , ML Pipelines Knowledge on Inferential descriptive statistics for a better understanding of mathematical underpinnings. Experience using various databases for data ingestion, and data manipulation viz. RDBS ( SQL) , NoSQL(MongoDB) , AWS cloud (Lambda, S3, Redshift, RDS, DocumentDB etc) along with their context of usage. Working knowledge on Neural Networks viz. LSTM (RNN), various activation functions, hyperparameter tuning, evaluation metrics. Experience in ML flow built-in algorithms, Inference Pipeline, Sagemaker templates for CI/CD pipelines using CodeCommit, CodePipeLine, deploying custom docker images on Sagemaker pipeline. Experience in automating the ML pipelines using the AWS EventBridge, Step Functions, ML Lineage etc, and using CloudTrail for logging and CloudWatch metrics for Model drift identification post model deployment

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