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BDO is a firm built on a foundation of positive relationships with our people and our clients. Each day, our professionals provide exceptional service, helping clients with advice and insight they can trust. In turn, we offer an award-winning environment that fosters a with a high priority on your personal and professional growth.
Your Opportunity
BDO Digital is seeking an experienced and technically proficient Senior ML Engineer to join our Technology Advisory Services practice. This role blends technical leadership with hands-on MLOps development, focusing on delivering scalable, secure, and production-grade machine learning pipelines using Azure and Databricks. The successful candidate will also lead client engagements, aligning ML initiatives with business objectives to create real-world impact.
Key Responsibilities
MLOps and Platform Development
1. Design and implement end-to-end MLOps pipelines using Databricks, MLflow, and related tools.
2. Build and manage scalable data and feature engineering pipelines in Databricks.
3. Automate model lifecycle processes including training, testing, deployment, and monitoring.
4. Implement CI/CD workflows using tools such as Azure DevOps or GitHub Actions.
5. Ensure operational reliability, performance, and compliance across all ML workflows.
Client and Delivery Management
6. Serve as a primary technical lead for client engagements focused on ML solution delivery.
7. Translate business goals into machine learning strategies and operational plans.
8. Collaborate with cross-functional teams to integrate models into production environments.
Machine Learning Application
9. Convert data science prototypes into robust, scalable ML solutions.
10. Apply appropriate ML algorithms to structured and unstructured data problems.
11. Evaluate model performance, run experiments, and iterate for improvement.
12. Document ML pipelines and contribute to internal knowledge sharing.
Qualifications
Required
13. Educational Background
A Bachelor’s or master's degree in computer science, Data Science, Engineering, or a closely related discipline. A strong academic foundation in algorithms, data structures, machine learning, and distributed systems is essential.
14. Professional Experience
A minimum of 5 years of hands-on experience in software engineering, data engineering, or DevOps, including at least 3 years of direct experience in MLOps or machine learning engineering roles. Proven success in deploying and maintaining machine learning solutions in production environments is expected.
15. In-depth knowledge of the Microsoft Azure ecosystem, with demonstrated experience using services such as Azure Machine Learning, Azure Data Lake, Azure Kubernetes Service (AKS), and Azure DevOps. Ability to leverage cloud-native tools to build scalable and secure ML workflows.
16. Very Strong proficiency with Databricks, including hands-on work with Delta Lake, MLflow, and Apache Spark. Experience integrating these tools into MLOps pipelines and optimizing performance and reliability in production.
17. Advanced programming skills in Python, with practical experience using popular machine learning libraries such as scikit-learn, TensorFlow, and/or PyTorch. Capable of building, tuning, and deploying ML models in real-world applications.
18. Solid understanding of CI/CD practices, with experience designing and maintaining pipelines using tools like GitHub Actions, Azure DevOps, or Jenkins. Familiarity with infrastructure-as-code tools such as Terraform or ARM templates for automating environment provisioning and deployment.
19. Excellent written and verbal communication skills, with the ability to effectively engage with a range of stakeholders, including data scientists, engineers, business partners, and executive leadership. Proven ability to explain technical concepts to non-technical audiences and influence decision-making.
Preferred
20. Certifications
Professional certifications such as Databricks Certified Professional or Microsoft Certified, e.g. Azure Solution Architect.
21. Strong knowledge of LLM frameworks and libraries (such as transformers, trl, deepspeed, PyTorch), and exposure to various ML techniques and their practical implementation in production at large scale.
22. Consulting experience and someone with excellent communication and client management skills
.