About the role
Job Title: Machine Learning ( MLOps) & Data Engineer
Experience: 5 - 7 Years
Position Summary
We are seeking a highly skilled Senior Machine Learning & MLOps / Data Engineer with 57 years of enterprise experience to join our CIP project team. In this role, you will design, deploy, and scale enterprise data engineering pipelines, automate ML model lifecycles, and establish robust MLOps practices across modern cloud environments.
Key Responsibilities
- Pipeline & Architecture Engineering: Design, build, and maintain large-scale distributed data engineering pipelines using Python, Spark, Databricks, and orchestration engines like Apache Airflow.
- MLOps & Model Lifecycle Management: Implement end-to-end MLOps solutions, including model lifecycle tracking, feature store integration, automated experimentation, and robust production deployment strategies.
- Infrastructure & CI/CD: Establish automated deployment pipelines (CI/CD) via Azure DevOps, GitHub Actions, or Jenkins. Containerize applications using Docker and Kubernetes.
- Cloud & Big Data Infrastructure: Manage distributed data workloads using cloud platforms (AWS, Azure, or GCP) alongside technologies like Apache Spark, Kafka, or Flink.
- Software Engineering Standards: Enforce high code-quality standards through version control, automated testing, secure coding practices, design patterns, and peer code reviews.
- Governance & Security: Ensure all data and model infrastructure adheres to enterprise data governance, privacy, security, and compliance protocols.
Qualifications & Skill Requirements
Must-Have (Required):
- Education: Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or a related field.
- Experience: 5–7 years of hands-on experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering.
- Big Data Systems: Proven experience with distributed systems like Apache Spark, Kafka, Hadoop, or Flink.
- Data Pipelines: Proven expertise building and managing large-scale data pipelines using Python, Databricks, Airflow, or similar stacks.
- Cloud Platforms: Hands-on experience with AWS, Azure, or GCP.
- DevOps & Containers: Strong proficiency in Docker, Kubernetes, and continuous integration/delivery (Azure DevOps, GitHub Actions, or Jenkins).
- Software Fundamentals: Deep understanding of object-oriented design, design patterns, testing strategies, and secure development.
- ML Lifecycle: Hands-on experience in feature management, experiment tracking, and production model deployment.
- Soft Skills: Excellent problem-solving, communication, and multi-project prioritization skills.
Nice-to-Have (Desired):
- Certifications: Professional certifications in Cloud (AWS/Azure/GCP), Data Engineering, MLOps, or Machine Learning.
- Advanced Data Platforms: Experience with Snowflake, Delta Lake, Feature Stores, or Vector Databases.
- AI & Machine Learning Frameworks: Exposure to TensorFlow, PyTorch, Scikit-learn, MLflow, or Generative AI / LLM / RAG / Agentic AI solutions.
- IaC & Observability: Familiarity with Terraform/Bicep/CloudFormation and monitoring tools like Grafana, Prometheus, ELK, or Datadog.
- Leadership & Process: Experience working in Agile/Scrum environments and mentoring junior engineers.
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