Sr. Machine Learning Engineer

Requisition Number: 103143

As a Senior Machine Learning Engineer at SADA, you will work in a fast-paced, highly-skilled team developing state-of-the-art enterprise solutions. You will work collaboratively with architects and other engineers to recommend, prototype, build, and debug machine learning (ML) solutions on Google Cloud Platform (GCP) for SADA's clients. You will work on real-world data problems, designing and implementing cloud-native ML and AI solutions, with a strong focus on Google’s Vertex AI platform and related technologies.

This is a hands-on role that includes both architecting and implementing ML/AI solutions, from initial requirements gathering through final model deployment, including iterative measurement and improvement.

You will be responsible for building and deploying ML solutions using cloud ML pipelines, often collaborating with data scientists and data engineers. This role often involves working with large volumes of structured and unstructured data from multiple sources in multiple modalities, requiring you to design and implement data pipelines to clean and merge data for research and modeling. You will provide subject matter expertise and assistance to SADA's clients and your colleagues on artificial intelligence and machine learning topics, participating in client-facing discussions and assisting with technical pre-sales discussions.

As an established contributor, you will develop critical components for ML projects, often working independently with minimal oversight.

Machine learning is an extremely dynamic and quick-changing discipline, requiring a passion for continuous learning and a knack for problem-solving. Your approach for learning new technology will be as important as any prior knowledge. While your core focus will be on machine learning and MLOps, the field increasingly intersects with broader artificial intelligence and generative AI concepts. Therefore, familiarity and practical experience with advanced AI topics such as LLM fine-tuning, agentic AI development and multimodal data processing are highly valued and contribute significantly to success in this role.

Your day-to-day will consist of the following activities:

• Implementing machine learning solutions for SADA clients across all project stages, from requirements gathering, discovery, and architecture, to development, testing, and final model deployment, including iterative measurement and improvement
• Recommending, prototyping, building, and debugging machine learning (ML) infrastructures on Google Cloud Platform (GCP)
• Working on real-world data problems by developing custom models or using existing Google ML APIs
• Building and deploying ML solutions using cloud ML pipelines
• Providing subject matter expertise and assistance to SADA clients and colleagues on machine learning topics
• Creating technical documentation and leading solution design discussions
• Assisting with technical pre-sales discussions and the creation of statements of work

Customer-Facing - Daily interactions with customers, including standups or sales meetings; at other times, weekly/bi-weekly as for sprint planning, reviews/retrospectives, or demos. These interactions will be with roles ranging from engineers to key executive stakeholders.

Travel - Up to 35% travel to customer sites, conferences, and other related events. Otherwise, this is a remote position and we expect candidates that have demonstrated success working in remote, distributed teams.

• Google Professional Machine Learning Certification (within 90 days of hire)

• 4+ years of relevant work experience designing and implementing Machine Learning solutions, preferably in a technical consulting environment
• Proficiency in Python with extensive experience across data manipulation (e.g., Pandas, Polars, Num Py), scientific computing (e.g., Sci Py), and machine learning frameworks (e.g., Scikit-learn, XGBoost, Tensor Flow, PyTorch)
• Strong foundational understanding of machine learning principles and hands-on experience with various model types, including supervised, unsupervised, and deep learning architectures
• Experience with data exploration, cleaning, and feature engineering from diverse, often messy, datasets to prepare them for modeling. This includes experience processing, cleansing, and verifying data integrity for analysis
• Familiarity with MLOps practices and tools for model deployment, monitoring, versioning, and pipeline orchestration (e.g., Docker, Kubernetes, Kubeflow, MLflow, Airflow)
• Solid understanding of the Google Cloud Platform for data storage (e.g., Cloud Storage, Big Query), compute (e.g., Cloud Run, Google Kubernetes Engine), and ML services, particularly Vertex AI and BigQuery ML
• Experience working with interactive development environments like Jupyter Lab and Vertex AI Workbench
• Excellent problem-solving skills with a demonstrated ability to tackle…