United States onlyThis position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior Data Engineer, MLOps in the United States.
This role offers an exciting opportunity to lead the development and deployment of robust machine learning infrastructure in a fast-paced, innovative environment. You will partner with data scientists, engineers, and product teams to operationalize data-driven solutions, streamline ML pipelines, and ensure high-quality model delivery. The position focuses on end-to-end MLOps, including automation, feature store management, real-time inference, monitoring, and CI/CD integration. You will design scalable, resilient systems that accelerate time-to-market for ML models, while implementing best practices for governance, reproducibility, and reliability. This role is ideal for a technically strong engineer who thrives in collaborative, high-impact environments and enjoys solving complex ML engineering challenges.
Accountabilities:
- Design, implement, and maintain ML pipelines and data engineering workflows using AWS services such as SageMaker, Step Functions, and EKS.
- Operationalize key data science solutions for underwriting, pricing, claims routing, and marketing applications.
- Build and manage a shared feature store supporting both batch and real-time feature retrieval.
- Develop real-time inference services with low-latency endpoints and deploy using blue/green or canary strategies.
- Implement testing, monitoring, and validation strategies within CI/CD pipelines to ensure platform reliability and performance.
- Enable ML governance, including model versioning, experiment tracking, reproducibility, and automated retraining based on drift or business events.
- Collaborate with data scientists, engineers, and cross-functional teams to deliver scalable, production-ready ML solutions.
Requirements
- Bachelor’s degree or equivalent relevant experience.
- Minimum 8 years of industry experience, with at least 2 years in MLOps and 2 years in software engineering.
- Proficiency in Python and Docker; familiarity with build tools such as Bash and Bazel.
- Strong experience in IaC principles and tools, particularly Terraform.
- Demonstrated expertise in designing, deploying, and managing scalable MLOps solutions on AWS.
- Experience with the full ML lifecycle, including data ingestion, preprocessing, model training, deployment, and production monitoring.
- Proficiency in designing workflows using tools like AWS Step Functions.
- Experience implementing CI/CD pipelines tailored for machine learning systems.
- Strong collaborative skills and excellent written and verbal communication.
- Bonus: experience in distributed systems, complex APIs, Snowflake Snowpark, or regulated industries like insurance.
Benefits
- Competitive salary range: $213,000 to $300,000 annually, based on skills and experience.
- Comprehensive health coverage including medical, dental, vision, and life insurance.
- Headspace subscription, monthly wellness allowance, and 401(k) plan with company match.
- One-time $2,000 stipend for home office setup, plus fully provisioned MacBook Pro.
- Four weeks PTO in the first year, plus twelve weeks paid parental leave for new parents.
- Professional development budget up to $5,000 annually, LinkedIn Learning subscription, and coaching opportunities.
- Remote-first work environment with core collaboration hours from 9 AM–2 PM Pacific time.
Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching.
When you apply, your profile goes through our AI-powered screening process, designed to identify top talent efficiently and fairly.
🔍 Our AI evaluates your CV and LinkedIn profile thoroughly, analyzing your skills, experience, and achievements.
📊 It compares your profile to the job’s core requirements and past success factors to determine your match score.
🎯 Based on this analysis, we automatically shortlist the three candidates who best match the role.
🧠 When necessary, our human team may conduct an additional manual review to ensure no strong profile is overlooked.
The process is transparent, skills-based, and free of bias — focusing solely on your fit for the role. Once the shortlist is complete, we share it directly with the company that owns the job opening. The final decision and next steps (such as interviews or assessments) are made by their internal hiring team.
