Senior Machine Learning Engineer

Job Description

Your Role:

The ML/MLOps Engineering team is responsible for architecting, deploying, and scaling enterprise AI/ML solutions while providing technical leadership, architectural guidance, and engineering best practices across the machine learning lifecycle. The team focuses on building reusable ML frameworks and platform capabilities, optimizing and refactoring large-scale PySpark workloads, tuning Spark cluster configurations for performance and cost efficiency, and enabling scalable, production-ready ML systems. We are seeking a highly capable Senior MLOps Engineer with 6-10 years of experience in Software Engineering, MLOps, DevOps, Cloud Platforms, and Distributed Data Processing. The candidate will have proven experience collaborating directly with clients and stakeholders to gather requirements, define solution architectures, drive technical discussions, and deliver scalable, secure, and reliable machine learning platforms that accelerate business value and enterprise AI adoption.

• Build Associate closely working with business stakeholders, data scientists, and engineering teams to understand business requirements and translate them into scalable AI/ML and data engineering solutions.
• Lead technical discussions, solution design workshops, and architectural reviews to define end-to-end ML and MLOps implementation strategies.
• Design, build, and maintain scalable, secure, and production-ready ML platforms and infrastructure across cloud environments.
• Develop reusable frameworks, templates, and best practices to accelerate model development, deployment, and operationalization.
• Optimize and refactor large-scale PySpark applications to improve performance, scalability, reliability, and cost efficiency.
• Configure, tune, and manage Spark clusters, including executor sizing, resource allocation, partitioning strategies, caching, and workload optimization.
• Design, implement, and maintain CI/CD pipelines for automated model training, testing, deployment, and monitoring.
• Establish and enforce MLOps best practices, including version control, experiment tracking, model registry, governance, and reproducibility.

Your profile:

• Master’s in computer science, data science, data engineering or a related field.
• 6-10 years of experience in Data Engineering or MLOps.
• Strong hands-on experience with PySpark optimization and cluster performance tuning.
• Experience with Azure Databricks, Apache Spark, Azure Machine Learning and Azure DevOps.
• Proficiency in Python, SQL and CI/CD tools.
• Experience with Agile Software Development.
• Proven experience in developing and deploying Supervised Machine Learning models, candidate will be working on Price Recommendation and Billing Recommendation systems.
• Experience building and managing production-grade ML pipelines and enterprise AI platforms.
• Strong client-facing communication skills with experience gathering requirements, managing stakeholder expectations, and delivering technical solutions.
• Ability to balance architecture, hands-on development, operational support, and strategic planning.

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