Machine Learning Ops Architect_13405 All 5 location Full-Time

1 week ago

Bengaluru, Karnataka, 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

Other Details

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