While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.
If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!
Years of experience: 3+ yrs (3 yrs to 10 yrs)
Location: Bangalore - Hybrid only
We are seeking a skilled and passionate ML Engineer with 3+ years of advanced ML Ops framework deployment 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 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 to 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 for experiment tracking, model versioning, and model registry.
Deploy and manage ML models in production environments on Databricks, leveraging services like ML Flow, Github actions , Unity Catalog, Databricks asset bundle
Hands on exposure in using Databricks workflow as an orchestrator to create multi task workflows for trainings and inference pipelines
Experience in handling Mosaic AI model serving and leverage lakehouse monitoring for model drift
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 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.
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.
Strong proficiency in Python programming for data manipulation, machine learning, and scripting.
Hands-on experience with machine learning frameworks such as Scikit-learn, TensorFlow, PyTorch, or Keras.
Demonstrated experience with MLflow for experiment tracking, model management, and model deployment.
Proven experience working with Microsoft Azure cloud services, specifically Azure Machine Learning, Azure Databricks, and related compute/storage services.
Solid experience with Databricks for data processing, ETL, and ML model development.
Understanding of MLOps principles and practices, including CI/CD for ML, model versioning, monitoring, and retraining.
Experience with containerization technologies (Docker) and orchestration (Kubernetes, especially AKS) for deploying ML models.
Familiarity with data warehousing concepts and SQL.
Ability to work with large datasets and distributed computing frameworks.
Strong problem-solving skills and attention to detail.
Excellent communication and collaboration skills.
Experience with other cloud platforms (AWS, GCP).
Knowledge of big data technologies like Apache Spark.
Experience with Azure DevOps for CI/CD pipelines.
Familiarity with real-time inference patterns and streaming data.
Understanding of responsible AI principles (fairness, explainability, privacy).
Microsoft Certified: Azure AI Engineer Associate
Databricks Certified Machine Learning Associate (or higher)
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
