Machine Learning Engineer

CoSourcing Partners - Enterprise-AI and IT Services Company; United States; On-site; USD 100,000 - 140,000; Full time

Job summary

A leading technology services firm in the United States is seeking a professional to productionize machine learning systems. This role focuses on building and maintaining MLOps pipelines and ensuring deployment of robust ML models in production environments. Ideal candidates will have a strong engineering background in Python and experience with tools like MLflow and Kubernetes. Join a team that values diversity, equity, and inclusion while fostering innovative ML solutions.

Qualifications

• Proven delivery of production ML pipelines, not just experiments.
• Experience in building CI/CD for ML models in Kubernetes.
• Hands-on with MLflow, Kubeflow, or Airflow.

Responsibilities

• Productionize machine learning at scale.
• Build MLOps pipelines and deliver compliant ML systems.
• Mentor junior engineers and establish ML engineering standards.

Skills

Production ML pipelines delivery

CI/CD for ML models

Monitoring and version governance

Kubernetes environments

Strong Python engineering background

Tools

MLflow

Kubeflow

Airflow

Prometheus

Grafana

Job description

You are being hired to productionize machine learning at scale — eliminating fragile pilot models, building hardened MLOps pipelines, and delivering compliant, monitored, and continuously improving ML systems that directly support business operations.

Success is measured not by “knowing tools,” but by deploying, stabilizing, and scaling real ML systems in production.

First-Year Outcomes (What You Must Deliver)

Within First 30 Days

• Fully assess current ML pipelines, data flows, and deployment architecture
• Identify top 3 reliability, security, and performance risks in current ML lifecycle

Within 90 Days

• Stand up standardized CI/CD pipelines for model training, validation, and deployment
• Implement automated monitoring, alerting, and versioning across active production models
• Deploy at least one business‑critical ML model into hardened production pipelines
• Establish security, audit, and compliance controls for model governance
• Reduce model deployment cycle time by 30–50%

Within 180 Days

• Operate a fully standardized enterprise MLOps framework (MLflow/Kubeflow/Airflow based)
• Enable continuous retraining and automated rollback capability
• Establish retraining cadence that improves model accuracy and reliability quarter‑over‑quarter
• Mentor junior engineers and codify ML engineering standards

Ongoing Success Metrics

Automated pipeline coverage: 100%

Compliance audit readiness: Continuous

What You Will Build

• Kubernetes‑based model serving platforms
• Model observability and alerting using Prometheus / Grafana

Required Experience (Performance Evidence)

• Proven delivery of production ML pipelines (not just experiments)
• Built CI/CD for ML models in Kubernetes environments
• Implemented monitoring, retraining, and version governance
• Delivered at least one enterprise‑scale ML deployment
• Hands‑on experience with MLflow / Kubeflow / Airflow
• Strong Python engineering background

CoSourcing Partners is an equal opportunity employer committed to fostering an inclusive and diverse workplace. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, genetic information, or any other protected status under applicable federal, state, or local laws.

We believe in creating a work environment where all employees feel valued, respected, and empowered to contribute to our success. Accommodations are available upon request for applicants with disabilities throughout the hiring process.

Join us and be part of a team that values diversity, equity, and inclusion.