Amazon Web Services (AWS)We are seeking a skilled and passionate ML Engineer with 3+ years of experience to join our team. The ideal candidate will be instrumental in developing, deploying, and maintaining machine learning models, with a strong focus on MLOps practices.
This role requires hands-on experience with Azure cloud services, Databricks, and MLflow to build robust and scalable ML solutions.
Responsibilities
- Design, develop, and implement machine learning models and algorithms to solve complex business problems.
- Collaborate with data scientists to transition models from research and development into production-ready systems.
- Build and maintain scalable data pipelines for ML model training and inference using Databricks.
- Implement and manage the ML model lifecycle using MLflow, including experiment tracking, model versioning, and model registry.
- Deploy and manage ML models in production environments on Azure, leveraging services such as:
- Azure Machine Learning
- Azure Kubernetes Service (AKS)
- Azure Functions
- Support MLOps workloads by automating model training, evaluation, deployment, and monitoring processes.
- Ensure the reliability, performance, and scalability of ML systems in production.
- Monitor model performance, detect model drift, and implement retraining strategies.
- Collaborate with DevOps and Data Engineering teams to integrate ML solutions into existing infrastructure and CI/CD pipelines.
- Document model architecture, data flows, and operational procedures.
Qualifications
Education
- Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, or a related quantitative field.
Experience
- Minimum 3+ years of professional experience as an ML Engineer or in a similar role.
Required Skills
- Strong proficiency in Python for data manipulation, machine learning, and scripting.
- Hands-on experience with machine learning frameworks, such as:
- Scikit-learn
- TensorFlow
- PyTorch
- Keras
- Demonstrated experience with MLflow for:
- Experiment tracking
- Model management
- Model deployment
- Proven experience working with Microsoft Azure cloud services, specifically:
- Azure Machine Learning
- Azure Databricks
- Related compute and storage services
- Solid experience with Databricks for:
- Data processing
- ETL pipelines
- ML model development
- Strong understanding of MLOps principles and practices, including:
- CI/CD for ML
- Model versioning
- Model monitoring
- Model retraining
- Experience with containerization and orchestration technologies, including:
- Docker
- Kubernetes (especially AKS)
- Familiarity with SQL and data warehousing concepts.
- Experience working with large datasets and distributed computing frameworks.
- Strong problem-solving skills and attention to detail.
- Excellent communication and collaboration skills.
Nice-to-Have Skills
- Experience with other cloud platforms (AWS or GCP).
- Knowledge of big data technologies such as Apache Spark.
- Experience with Azure DevOps for CI/CD pipelines.
- Familiarity with real-time inference patterns and streaming data.
- Understanding of Responsible AI principles, including fairness, explainability, and privacy.
Certifications (Preferred)
- Microsoft Certified: Azure AI Engineer Associate
- Databricks Certified Machine Learning Associate (or higher)
Shubham Vishwakarma
Full Stack Developer - Averlon
About Quantiphi
About
Quantiphi is an award-winning AI-first digital engineering company driven by the desire to reimagine and realize transformational opportunities at the heart of the business. Since its inception in 2013, Quantiphi has solved the toughest and most complex business problems by combining deep industry experience, disciplined cloud, and data-engineering practices, and cutting-edge artificial intelligence research to achieve accelerated and quantifiable business results.
Tech stack
NodeJS (Node.js)
PythonCompany video
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Bengaluru, Mumbai, and Trivandrum
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Know more:http://bit.ly/az-godigital">http://bit.ly/az-godigital
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Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon