AI/ML Architect

Company Description

About Sutherland

Artificial Intelligence. Automation.Cloud engineering. Advanced analytics.For business leaders, these are key factors of success. For us, they’re our core expertise.

We work with iconic brands worldwide. We bring them a unique value proposition through market-leading technology and business process excellence.

We’ve created over 200 unique inventions under several patents across AI and other critical technologies. Leveraging our advanced products and platforms, we drive digital transformation, optimize critical business operations, reinvent experiences, and pioneer new solutions, all provided through a seamless “as a service” model.

For each company, we provide new keys for their businesses, the people they work with, and the customers they serve. We tailor proven and rapid formulas, to fit their unique DNA.We bring together human expertise and artificial intelligence to develop digital chemistry. This unlocks new possibilities, transformative outcomes and enduring relationships.
Sutherland

Unlocking digital performance. Delivering measurable results.

Job Description

We are seeking an experienced AI/ML Architect with strong expertise in machine learning system design, production model deployment, and hands-on coding ability. The ideal candidate will be highly skilled in architecting end-to-end ML solutions, conducting code reviews across ML pipelines, and delivering scalable predictive analytics systems for enterprise clients. This is a hands-on architecture role requiring both technical depth and delivery leadership.

Key Responsibilities:

• Design, develop, and maintain end-to-end ML architectures covering data ingestion, feature engineering, model training, deployment, and monitoring.
• Perform code reviews across ML pipelines, model implementations, and deployment scripts to maintain engineering quality standards.
• Build and deploy production machine learning models using frameworks such as LightGBM, XGBoost, scikit-learn, PyTorch, and TensorFlow.
• Implement MLOps practices including model versioning, monitoring, retraining pipelines, and drift detection.
• Optimize model performance, diagnose data quality issues, and resolve production model degradation.
• Translate ambiguous client problem statements into technically sound, deliverable ML solutions.
• Lead and mentor a team of ML engineers and data scientists, establishing coding and validation standards.
• Act as technical authority in client discussions, solution workshops, and pre-sales engagements.
• Scope and estimate new ML opportunities and support proposals and RFP responses.
• Working under a dynamic agile based environment.
• Strictly adhere to scrum framework and guidelines.
• Coordinate with multiple development teams.

Qualifications

Required Skills & Experience:

• Overall experience: 10+ Years
• 6+ years of professional experience in machine learning, data science, or applied AI, with 3+ years in an architect or technical lead capacity.
• Strong hands-on coding proficiency in Python with production ML framework experience.
• Demonstrated experience taking models from prototype to production, not limited to POC or research work.
• Proven ability to conduct code reviews across ML pipelines, data engineering, and model deployment.
• Hands-on experience with classical ML techniques including supervised learning, anomaly detection, time-series analysis, and imbalanced classification.
• Working knowledge of MLOps tooling and production model lifecycle management.
• Cloud platform experience (Azure, AWS, or GCP) including ML services and deployment infrastructure.
• Proficiency with SQL and large-scale data processing.
• Familiarity with version control systems (Git, SVN, etc.).
• Strong problem-solving skills and ability to work independently.

Preferred Skills:

• Experience with predictive maintenance, hardware failure prediction, or IoT/telemetry data.
• Exposure to log analytics, observability data, or large-scale unstructured data processing.
• Experience with Databricks, Spark, or similar distributed data platforms.
• Knowledge of REST API integration for model serving and inference endpoints.
• Client-facing consulting or services delivery background.
• Advanced degree (MS or PhD) in Computer Science, Statistics, Applied Mathematics, or related field.
• Experience in agile development environments.

Additional Information