IT Analyst V

Experis Technology Group; United States; On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Remote work

Job summary

ManpowerGroup Global, Inc. is seeking a Machine Learning Engineer (MLOps) to build and maintain production-grade ML infrastructure, pipelines, and deployment processes in a remote setup.

You will bridge software, data engineering, and ML, delivering scalable, secure systems with CI/CD for ML workflows, data pipelines, and model deployment across cloud environments.

Qualifications

• Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, or related field.
• 3-5 years of professional software or data engineering experience.
• Minimum 2 years of production ML experience.
• Strong Python development skills.
• Familiarity with SQL and relational databases.
• Experience with Azure, AWS, or GCP.
• Hands-on Docker experience.
• Experience building data pipelines/ETL processes.
• Experience deploying ML models to production.
• Knowledge of ML frameworks (PyTorch, Scikit-learn).
• Experience with Pandas, Spark, or dbt.

Responsibilities

• Build and maintain CI/CD pipelines for ML workflows.
• Automate model testing, deployment, monitoring, and version control.
• Deploy ML models as scalable APIs/microservices.
• Monitor model performance and data drift in production.
• Support reliable AI operations in production.
• Develop and optimize ETL/data processing pipelines.
• Transform datasets for training and inference; support feature store.
• Collaborate to integrate ML outputs into enterprise apps.
• Write clean, maintainable Python code; participate in code reviews.

Skills

Python

Problem solving

Team collaboration

Production reliability mindset

Education

Bachelor's or Master's degree in CS/SE/Data Engineering

Tools

Docker

Kubernetes

SQL

Pandas

Spark

dbt

MLflow

Airflow

LangChain

PyTorch

Scikit-learn

Job description

Machine Learning Engineer (MLOps) | Remote | Contract-to-Hire

About the Opportunity

We are seeking a Machine Learning Engineer to help build and maintain the infrastructure, pipelines, and deployment processes that power AI and machine learning solutions in a healthcare technology environment. This is a contract-to-hire opportunity for candidates interested in long-term employment.

This role is ideal for someone who enjoys bridging the gap between software engineering, data engineering, and machine learning by building scalable, reliable, and secure production systems.

Responsibilities

MLOps & Model Deployment

• Build and maintain CI/CD pipelines for machine learning workflows
• Automate model testing, deployment, monitoring, and version control
• Deploy machine learning models as scalable APIs and microservices
• Monitor model performance, data drift, and system health in production environments
• Support reliable and stable AI operations in production

Data Engineering & Integration

• Develop and optimize ETL and data processing pipelines
• Transform complex datasets into clean, usable data for training and inference
• Support feature engineering and feature store development
• Ensure consistency between training and production environments
• Collaborate with engineering teams to integrate machine learning outputs into enterprise applications

Engineering Excellence

• Write clean, maintainable, and well-documented Python code
• Participate in code reviews and contribute to engineering best practices
• Utilize Docker and Kubernetes to package and deploy machine learning workloads
• Support secure deployment processes and compliance requirements
• Contribute to reliable, scalable, and highly available AI systems

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, or a related field
• 3-5 years of professional software engineering or data engineering experience
• Minimum 2 years of experience supporting machine learning solutions in production environments
• Strong Python development skills
• Familiarity with SQL and relational databases
• Experience with at least one major cloud platform such as Azure, AWS, or GCP
• Hands-on experience with Docker
• Experience building and maintaining data pipelines and ETL processes
• Experience deploying machine learning models into production environments
• Knowledge of machine learning frameworks such as PyTorch or Scikit-learn
• Experience with data processing technologies such as Pandas, Spark, or dbt

Preferred Qualifications

• Experience with MLflow, Airflow, Prefect, BentoML, Kubeflow, or similar MLOps tools
• Experience deploying and supporting Large Language Models (LLMs)
• Experience with LangChain, RAG architectures, or generative AI solutions
• Knowledge of Kubernetes
• Experience working in regulated industries such as healthcare or finance
• Familiarity with healthcare interoperability standards such as FHIR or HL7
• Experience designing APIs and microservices architectures

What We're Looking For

• Strong problem-solving and troubleshooting skills
• Passion for building reliable production AI systems
• Experience collaborating across engineering, data, and business teams
• Ability to thrive in a fast-paced environment while maintaining high quality standards
• Focus on scalability, automation, security, and operational excellence

Additional Details

• Contract-to-hire opportunity
• Candidates must be eligible for permanent employment without sponsorship constraints
• Full-time, 40 hours per week
• Remote work available
• Initial contract expected through February 2027 with long-term hiring intent

Keywords: ML Engineer, MLOps Engineer, Machine Learning Engineer, AI Engineer, Data Engineer, Python, Azure, AWS, GCP, Docker, Kubernetes, MLflow, Airflow, LangChain, LLM, Generative AI, Spark, ETL, Healthcare Data, FHIR, HL7, MLOps, Model Deployment.