Machine Learning Engineer

Job Overview
We're looking for an ML Engineer to join our delivery team and serve as a critical bridge between Data Science, Data Engineering, and cloud practices. In this role, you'll help translate experimental models into production-ready systems, strengthen our engineering standards, and ensure our client solutions are robust, scalable, and maintainable.
This is a mid to senior level position suited for someone who understands the full lifecycle of machine learning systems—from prototype to production—and who cares deeply about code quality, reproducibility, and operational excellence. You'll work across multiple client engagements, bringing consistency and rigor to how we build and deploy ML solutions.
Role Expectations
Delivery Quality & Engineering Rigor

• Own the productionization of machine learning models, ensuring they meet performance, reliability, and maintainability standards before deployment
• Establish and enforce best practices for code review, testing, version control, and documentation across ML projects
• Identify technical debt and quality gaps in existing solutions and drive improvements

Bridging Disciplines

• Collaborate closely with Data Scientists to understand model requirements and translate experimental code into production-grade implementations
• Partner with Data Engineers to ensure feature pipelines, data transformations, and model inputs are well-architected and performant
• Work with DevOps and platform teams to design and implement MLOps workflows including CI/CD, model monitoring, and automated retraining

Solution Development

• Design and build ML infrastructure and pipelines on AWS using services such as SageMaker, Lambda, Step Functions, ECS/EKS, and related tooling
• Implement model serving solutions that balance latency, cost, and scalability requirements
• Develop frameworks and reusable components that accelerate delivery across client engagements

Client Engagement

• Participate in technical discussions with clients to understand requirements and communicate architectural decisions
• Contribute to solution design and technical proposals
• Mentor junior team members and help raise the technical bar across the organization

Qualifications
Required

• 3+ years of experience in software engineering, machine learning engineering, or a related field
• Strong programming skills in Python with an emphasis on writing clean, testable, well-documented code
• Hands-on experience deploying and maintaining ML models in production environments
• Proficiency with AWS services, particularly those related to data and ML workloads
• Solid understanding of ML fundamentals including model training, evaluation, feature engineering, and common algorithms
• Experience with containerization (Docker) and orchestration tools
• Familiarity with MLOps practices including experiment tracking, model versioning, CI/CD for ML, and monitoring
• Strong communication skills and the ability to work effectively with cross-functional teams

Preferred

• Experience in a consulting or professional services environment
• Familiarity with infrastructure-as-code tools such as Terraform or CloudFormation
• Experience with distributed computing frameworks like Spark
• Background in Data Engineering or Data Science that complements ML Engineering expertise
• AWS certifications related to Machine Learning or Data Analytics