United States onlyThis position is posted by Jobgether on behalf of a partner company. We are currently looking for a Machine Learning Engineer in Texas (USA).
In this role, you will design, develop, and maintain end-to-end MLOps pipelines, enabling scalable, secure, and reliable deployment of machine learning models. You will collaborate with data scientists, software engineers, and platform teams to operationalize models and integrate them into production systems. The position offers the opportunity to shape and evolve MLOps practices, implement best-in-class CI/CD workflows, and ensure model reliability through observability and automated retraining. You will work in a fast-paced, cross-functional environment with a focus on reproducibility, governance, and high-quality software delivery. This role emphasizes both hands-on engineering and strategic platform development to accelerate machine learning adoption across the organization.
Accountabilities:
- Design, build, and maintain MLOps pipelines covering data preparation, model training, validation, packaging, and deployment.
- Develop FastAPI microservices for model inference, ensuring clear API contracts, versioning, and documentation.
- Define and implement deployment strategies on AKS using GitOps practices with Argo CD, including blue/green, canary, and rollback workflows.
- Architect and evolve a self-serve MLOps platform with standards, templates, and scaffolding for repeatable, secure model delivery.
- Operationalize ML frameworks (scikit-learn, PyTorch, XGBoost) for low-latency, scalable inference.
- Implement CI/CD workflows for ML, including automated testing, security scanning, packaging, and promotion.
- Integrate telemetry and observability tools, establish SLOs, and monitor model/data drift, automating retraining and evaluation processes.
- Collaborate with cross-functional teams to ensure seamless integration of ML services into applications and shared platforms.
- Champion best practices for code quality, reproducibility, and governance, including model registry and approvals.
Requirements
- Strong Python engineering skills with production experience building FastAPI services.
- Proven MLOps experience: serving, scaling, and maintaining models as APIs in production.
- Hands-on experience with CI/CD for ML, automated testing, and release pipelines using GitHub Enterprise or similar.
- Expertise with containerization (Docker) and orchestration (Kubernetes), with deployments on AKS.
- Practical experience with GitOps using Argo CD and deployment strategies (blue/green, canary, rollback).
- Solid understanding of RESTful API design, microservices patterns, and API contract governance.
- Experience designing or contributing to MLOps platforms with standards, templates, and tooling for repeatable model delivery.
- Ability to work collaboratively across data science, software engineering, and platform/SRE teams.
- Preferred: 5+ years of relevant experience, familiarity with MLflow or similar tracking/registry tools, feature stores, Databricks, Azure/GCP, Agile practices, Helm/Kustomize, secrets management, and security scanning.
Benefits
- Competitive compensation for a 6-month contract with potential for extension.
- Remote-friendly role (PST preferred) with flexibility for cross-time-zone collaboration.
- Exposure to cutting-edge MLOps practices and cloud technologies.
- Opportunity to influence ML infrastructure and best practices across teams.
- Collaborative environment focused on innovation, automation, and high-quality software delivery.
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 3 candidates with the highest match to the role.
🧠 When necessary, our human team may perform an additional manual review to ensure no strong profile is missed.
The process is transparent, skills-based, and free of bias — focusing solely on your fit for the role. Once the shortlist is completed, we share it directly with the company that owns the job opening. The final decision and next steps (such as interviews or additional assessments) are then made by their internal hiring team.
