Abhishek · Senior MLOps / Cloud Engineer · 6+ yrs
Mid-LevelProof of scale
About Abhishek
Abhishek is a Senior MLOps Engineer with 6+ years of experience in multi-cloud environments, specializing in MLOps and data engineering. He has a proven track record of optimizing machine learning model performance and implementing CI/CD pipelines.
6+ years of commercial experience in
Skills(24)
Why hire Abhishek?
Improved fraud detection accuracy by 35% through innovative MLOps practices.
Reduced time to production by 50% with automated CI/CD pipelines.
Enhanced data ingestion efficiency by 40% for serverless ML platforms.
Improved fraud detection accuracy by 35%.
Reduced time to production by 50%.
Increased deployment frequency by 60%.
Project highlights(7)
Real-Time Fraud Detection System – Senior MLOps Engineer
Overview: Developed a real-time fraud detection system leveraging Azure MLOps and Databricks. Responsibilities: Implemented an automated MLOps pipeline on Azure for model training, deployment, and monitoring using Azure DevOps, reducing time to production by 50%. Integrated MLflow for model tracking, versioning, and lifecycle management, ensuring traceability and compliance. Deployed containerized machine learning models on Azure Kubernetes Service (AKS) to enable scalability and high availability.
Key outcomes:
Improved fraud detection accuracy by 35%.
Reduced time to production by 50%.
Serverless Machine Learning Platform – MLOps Engineer
Overview: Built a serverless machine learning platform using AWS services for real-time model inference and data processing. Responsibilities: Automated data preprocessing and ETL pipelines using AWS Glue, enhancing data ingestion efficiency by 40%. Deployed models using AWS SageMaker and Lambda, reducing infrastructure costs by 45% through serverless architecture.
Key outcomes:
Enhanced data ingestion efficiency by 40%.
Reduced infrastructure costs by 45%.
Multi-Cloud Data Engineering and MLOps Pipeline – MLOps Specialist
Overview: Developed a multi-cloud data engineering and MLOps pipeline using Databricks, Apache Airflow, and Kubernetes. Responsibilities: Integrated data from Azure and AWS using Apache Airflow for ETL processes, reducing data pipeline latency by 55%. Containerized machine learning models and deployed them on Kubernetes (EKS) for scalable and high-availability deployments.
Key outcomes:
Reduced data pipeline latency by 55%.
Kubernetes-Driven Scalable AI Model Deployment – DevOps/MLOps Engineer
- Led the deployment of TensorFlow-based AI models using Kubernetes on EKS for e-commerce recommendation engines.
- Led the deployment of TensorFlow-based AI models using Kubernetes on EKS, ensuring seamless scaling and high availability for e-commerce recommendation engines.
- Automated the ML model deployment pipeline using Jenkins, Docker, and Terraform, reducing manual interventions and deployment errors by 70%.
- Configured Prometheus and Grafana for real-time monitoring of model performance and resource utilization, optimizing cloud resource usage by 40%.
- Implemented canary deployments to safely release new model versions, minimizing the risk of service interruptions.
- Worked closely with data scientists to optimize containerized AI models, improving inference speed by 25%.
Key outcomes:
Reduced manual interventions and deployment errors by 70%.
Optimized cloud resource usage by 40%.
Improved inference speed by 25%.
Hybrid Cloud Model for Predictive Maintenance Analytics – MLOps Engineer
- Developed a hybrid cloud-based predictive maintenance system integrating Azure and AWS for IoT devices.
- Developed a hybrid cloud-based predictive maintenance system integrating Azure and AWS, leveraging real-time sensor data from IoT devices.
- Built scalable data pipelines using Apache Kafka and Apache Spark to process and analyze large volumes of streaming data.
- Deployed machine learning models for predictive maintenance on a multi-cloud setup, balancing cost and performance between Azure and AWS.
- Implemented CI/CD pipelines using Jenkins and Docker, enabling rapid and reliable updates to predictive models in production.
- Automated infrastructure provisioning with Terraform to ensure reproducibility and scalability of the cloud environment.
Key outcomes:
Improved inference speed by 25% (from optimizing containerized AI models).
Industry experience
AI / ML Platform
6 projects- •Real-Time Fraud Detection System— Senior MLOps EngineerAzure MLOps · Python · Databricks · Kubernetes +2
- •Serverless Machine Learning Platform— MLOps EngineerAWS · Python · Docker · Terraform +1
- •Multi-Cloud Data Engineering and MLOps Pipeline— MLOps SpecialistDatabricks · Apache Airflow · Kubernetes · Python
- •Kubernetes-Driven Scalable AI Model Deployment— DevOps/MLOps EngineerKubernetes (EKS) · Docker · TensorFlow · AWS +5
- •Hybrid Cloud Model for Predictive Maintenance Analytics— MLOps EngineerAzure · AWS · Python · SQL +5
- •Automated Risk Scoring System Using Azure ML and AKS— Junior MLOps EngineerAzure ML · Python · Azure DevOps · Docker +3
Cybersecurity
1 project- •Real-Time Fraud Detection System— Senior MLOps EngineerAzure MLOps · Python · Databricks · Kubernetes +2
Manufacturing & Industrial
2 projects- •Real-Time Fraud Detection System— Senior MLOps EngineerAzure MLOps · Python · Databricks · Kubernetes +2
- •Hybrid Cloud Model for Predictive Maintenance Analytics— MLOps EngineerAzure · AWS · Python · SQL +5
SaaS / B2B
2 projects- •Serverless Machine Learning Platform— MLOps EngineerAWS · Python · Docker · Terraform +1
- •Automated Risk Scoring System Using Azure ML and AKS— Junior MLOps EngineerAzure ML · Python · Azure DevOps · Docker +3
At a Glance
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Typically responds within 4 business hours.
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Abhishek
Senior MLOps Engineer