Motion Recruitment
Lead Forward Deployed Engineer / AWS / Python
Job at a glance
- Pay
- $170,000 – $200,000
- Workplace
- On-site
- Employment
- Full-time
- Sector
- Engineering & Technology
- Posted
- Nov 3, 2026
- First found
- Nov 3, 2026, 4:03 PM ET
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About the job
A global organization specializing in performance-driven business solutions, combining strategic marketing, technology, data analytics, and artificial intelligence. The company partners with large enterprises to support business growth through innovative digital solutions, data-informed strategies, and advanced analytical capabilities. It has an established international workforce and a strong reputation for fostering an innovative, collaborative, and employee-focused workplace culture.
Searching for a Machine Learning Operations Engineer, this person will build CI/CD pipelines, manage cloud infrastructure, monitor production AI systems, and ensure machine learning models are deployed reliably and securely at scale. You'll partner directly with AI Engineers and technical leadership while remaining highly hands-on with cutting-edge AI technologies.
Required Skills & Experience
- Bachelor’s degree in computer science, engineering or a related field, or comparable experience
- 3-5 years of experience in DevOps, MLOps, data engineering, or a related infrastructure/operations role
- Databricks
- Cloud AI platforms (AWS preferred, including familiarities with services like Bedrock)
- Python and familiarity with CI/CD tooling
- Experience monitoring and observability tolling for production systems
Desired Skills & Experience
- Familiarity with LLM deployment considerations (latency, cost, versioning)
- Solid understanding of data analytics fundamentals; experience with SQL
What You Will Be Doing
Tech Breakdown
- 22% AWS
- 18% Python
- 15% Docker/Kubernetes
- 13% CI/CD tools
- 10% Databricks
- 10% Monitoring/observability
- 5% SQL
- 7% Other (Git, documentation, troubleshooting)
Daily Responsibilities
- 20% Cloud infrastructure management
- 18% CI/CD pipeline development and maintenance
- 17% AI/ML model deployment and management
- 15% Monitoring and observability
- 12% Troubleshooting and production support
- 8% Automation and Python scripting
- 5% Cost and performance optimization
- 5% Documentation and collaboration