Senior AI/ML MLOps Engineer

Cynet Systems Inc.; Atlanta (GA); On-site; USD 114,000 - 125,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k)

Health savings account

Disability and life insurance

Supplemental coverage

Identity theft protection

Pet insurance

Wellness programs

Employee assistance program

Career development opportunities

Paid sick leave

Job summary

Cynet Systems Inc. seeks an experienced AI/ML Engineer with a strong data science background to design, deploy, and manage scalable ML solutions across the full lifecycle.

You will operationalize models at scale, build automated pipelines, ensure reproducibility, and collaborate with cross-functional teams to deliver robust AI/ML offerings across AWS, Azure, and GCP environments.

Qualifications

• Strong grounding in data science, machine learning, statistics, and predictive modeling, including supervised/unsupervised techniques.
• Experience with feature engineering, model selection, hyperparameter tuning, validation, and performance assessment.
• Proficiency in Python and familiarity with SQL; experience with Scikit-learn, TensorFlow, PyTorch, or XGBoost.
• Ability to manage ML solutions from experimentation through production, with tracking, registries, lineage, reproducibility, deployment, monitoring, drift detection, and retraining.
• Hands-on knowledge of MLOps methods and tools like MLflow, Kubeflow, Azure ML, AWS SageMaker, Vertex AI, or Databricks.
• Experience building automated ML pipelines and connecting to CI/CD; familiarity with Docker, Kubernetes, Git, and Azure DevOps/GitHub Actions/Jenkins.
• Experience automating end-to-end ML lifecycles with Kubeflow and Vertex AI, tracking experiments and models with MLflow, and drift detection with Evidently.
• Experience with at least one cloud provider (Azure/AWS/GCP) plus data engineering, ETL pipelines, data quality, SQL/NoSQL, large datasets, and cloud storage.
• Preferred: experience with generative AI, LLMs, NLP, RAG, conversational AI, or LLMOps and GenAI lifecycle management.
• Preferred: Databricks or Spark, feature stores, and data/model versioning tools.
• Preferred: knowledge of model governance, explainability, responsible AI, or AI security; healthcare AI/ML; Agile/Scrum delivery.
• Preferred: end-to-end AI solution development including model development, fine-tuning, evaluation, data pipelines.

Responsibilities

• Design, develop, deploy, and manage ML solutions across the ML lifecycle.
• Operationalize models at scale with automated training/deployment pipelines, versioning, experiment tracking, monitoring, drift detection, and CI/CD.
• Collaborate with Data Scientists, Data Engineers, Cloud/DevOps, architects, and product stakeholders to deliver scalable AI/ML solutions.

Skills

Data science

Machine learning

Statistics

Feature engineering

Experiment tracking

Model governance

Deployment

Monitoring

Drift detection

Reproducibility

MLOps

Generative AI

NLP

Agile/Scrum

Tools

Scikit-learn

TensorFlow

PyTorch

XGBoost

MLflow

Kubeflow

Azure ML

AWS SageMaker

Vertex AI

Databricks

Docker

Kubernetes

Git

GitHub Actions

Jenkins

Evidently

Job description

Salary: $114,400 - 124,800 per year

Requirements

• Strong grounding in data science, machine learning, statistics, and predictive modeling, including practical knowledge of supervised and unsupervised techniques.
• Experience with feature engineering, model selection, hyperparameter tuning, validation, and performance assessment.
• Proficiency in Python and familiarity with SQL; experience using frameworks such as Scikit-learn, TensorFlow, PyTorch, or XGBoost.
• Demonstrated ability to manage machine learning solutions from experimentation through production, including experiment tracking, model registries and versioning, dataset and model lineage, reproducibility, deployment, monitoring, drift detection, and automated retraining.
• Hands-on knowledge of MLOps methods and tools, such as MLflow, Kubeflow, Azure Machine Learning, AWS SageMaker, Google Vertex AI, or Databricks.
• Experience developing automated ML pipelines and connecting them to CI/CD workflows; familiarity with Docker, Kubernetes, Git, and platforms such as Azure DevOps, GitHub Actions, or Jenkins.
• Experience automating end-to-end ML lifecycles with Kubeflow and Vertex AI, tracking and registering experiments and models with MLflow, and detecting data or model drift with Evidently.
• Experience with at least one of Azure, AWS, or GCP, plus knowledge of data engineering, ETL/ELT pipelines, data quality, SQL or NoSQL databases, large datasets, scalable data processing, and cloud storage.
• Preferred: experience with generative AI, LLMs, RAG, NLP, conversational AI, or LLMOps and GenAI lifecycle management.
• Preferred: experience with Databricks or Spark, feature stores, and data or model versioning tools.
• Preferred: knowledge of model governance, explainability, responsible AI, or AI security; healthcare AI/ML experience; and familiarity with Agile/Scrum delivery.
• Preferred: experience spanning model development and fine-tuning, evaluation, data pipelines, feature engineering, and end-to-end AI solution development.

Responsibilities

• Design, develop, deploy, and manage machine learning solutions throughout the ML lifecycle.
• Operationalize models at scale by building and maintaining automated training and deployment pipelines, model versioning and experiment tracking, monitoring and drift detection, and CI/CD processes.
• Collaborate with Data Scientists, Data Engineers, Cloud/DevOps teams, architects, and product stakeholders to deliver scalable, reliable AI/ML solutions.

Technologies

• AI
• AWS
• Azure
• CI/CD
• Cloud
• Databricks
• DevOps
• Docker
• ETL
• Fine-tuning
• GCP
• Git
• GitHub
• Jenkins
• Kubeflow
• Kubernetes
• Machine Learning
• MLflow
• MLOps
• NoSQL
• PyTorch
• Python
• RAG
• SQL
• Security
• Spark
• TensorFlow

More

We are seeking an experienced AI/ML Engineer with a strong data science background. The pay range is $55–$60 per hour. Paid sick leave is provided where required by law; benefit eligibility and terms vary by employment status, service length, and work location, and plans may be changed in accordance with applicable laws. We are Cynet Systems Inc., founded in 2010 and headquartered in the Washington, DC metro area. We provide technology staffing and workforce solutions to Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada, supporting IT, engineering, life sciences, clinical, and professional staffing. We are a certified Minority Business Enterprise and hold ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II certifications.

• medical insurance
• dental insurance
• vision insurance
• 401(k)
• health savings account
• disability and life insurance
• supplemental coverage
• identity theft protection
• pet insurance
• wellness programs
• employee assistance program
• career development opportunities
• Paid sick leave is provided where required by law