Machine Learning Engineer

We are seeking a highly skilled Machine Learning Engineering Consultant to join a collaborative AI/ML engineering team focused on building scalable, enterprise-grade AI solutions. This role is ideal for someone with strong hands-on ML engineering experience, cloud infrastructure expertise, and a passion for solving complex business problems with modern AI technologies.

This team operates in a highly collaborative POD-based environment, and they are looking for someone who is excited to contribute onsite alongside the team several days per week.

What You’ll Be Doing

• Design, build, deploy, and monitor scalable ML pipelines and AI solutions
• Develop and operationalize machine learning models in production environments
• Collaborate with data scientists, software engineers, and technical stakeholders to translate AI research into real-world applications
• Build robust and scalable cloud-native ML infrastructure and workflows
• Optimize ML models through tuning, testing, and continuous improvement
• Support CI/CD automation and DevOps processes for ML applications
• Evaluate emerging AI/ML tools, frameworks, and technologies
• Contribute to architectural decisions and mentor junior engineers when needed

Required Skills & Experience

• 5+ years of Machine Learning Engineering experience in production environments
• Strong Python programming skills
• Advanced SQL expertise
• Hands-on AWS cloud engineering experience

Experience with:

• Deep expertise in Core ML Engineering, including advanced Python programming, advanced SQL, and experience building scalable ML pipelines and AI solutions
• Strong AWS-focused Cloud & Infrastructure experience, including cloud-native application deployment and scalable ML architecture
• Proven experience with DevOps, CI/CD pipelines, automation, and Infrastructure as Code
• Demonstrated experience implementing and supporting real-world AI/ML solutions in production environments

Preferred / Nice-to-Have Skills

• Amazon Bedrock
• Amazon SageMaker
• AWS EKS / Kubernetes
• AWS Step Functions
• Apache Airflow
• Docker
• MLOps frameworks and practices
• CI/CD and DevOps automation experience
• Infrastructure as Code experience with tools such as Terraform, CloudFormation, Puppet, or GitHub workflows
• Experience deploying ML models into production environments
• Strong understanding of scalable system architecture and software engineering principles

What They’re Looking For

• Strong technical depth in ML Engineering and AI solutions
• Someone comfortable owning work end-to-end with minimal oversight
• Excellent communication and collaboration skills
• A team-oriented engineer who enjoys working closely with others onsite
• Experience working in Agile, fast-paced engineering environments