AI/ML Platform Engineer

Collaboration and Continuous Improvement: Translate business requirements into technical solutions, collaborate across multidisciplinary teams, evaluate emerging ML technologies, and improve platform reliability, scalability, usability, and operational efficiency. Implement automated testing, pipeline validation, staged deployment, and failure recovery using Apache Airflow, Kubeflow Pipelines, Prefect, or equivalent tools.

Title: AI/ML Platform Engineer
Rate – $50/hr.

Key Responsibilities

• ML Pipeline Automation and CI/CD: Build and maintain automated ML pipelines for data preprocessing, feature engineering, model training, evaluation, and deployment. Implement automated testing, pipeline validation, staged deployment, and failure recovery using Apache Airflow, Kubeflow Pipelines, Prefect, or equivalent tools.
• Model Development and Deployment: Prototype, implement, and optimize ML algorithms and integrate models into production systems. Apply appropriate supervised and unsupervised learning, deep learning, NLP, computer vision, and Generative AI techniques based on business requirements.
• Monitoring and Reliability: Implement logging, metrics, dashboards, and alerts to monitor pipeline health, model performance, data drift, model degradation, and infrastructure utilization. Participate in production support, incident response, troubleshooting, and root-cause analysis.
• Cloud Cost Optimization: Monitor compute, storage, and data-transfer costs. Optimize resource utilization through autoscaling, spot or preemptible instances, and workload scheduling. Contribute to infrastructure-as-code configurations, resource tagging, and budget controls.
• Developer Experience: Maintain internal SDKs, self-service tools, onboarding documentation, and reusable templates. Support platform users, troubleshoot issues, resolve bugs, and conduct technical enablement sessions for agency teams.
• Collaboration and Continuous Improvement: Translate business requirements into technical solutions, collaborate across multidisciplinary teams, evaluate emerging ML technologies, and improve platform reliability, scalability, usability, and operational efficiency.

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related technical field. A master's degree or PhD is preferred but not required.
• At least five (5) years of experience in software engineering, data engineering, or ML engineering, including at least two (2) years working with production ML systems.
• Strong proficiency in Python and experience developing maintainable, production-quality code.
• Solid understanding of ML and deep learning fundamentals, including model training, evaluation, feature engineering, versioning, and deployment.
• Experience with ML frameworks and libraries such as PyTorch, TensorFlow, or scikit-learn, and with at least one ML orchestration tool: Apache Airflow, Kubeflow Pipelines, or Prefect.
• Familiarity with AWS, Azure, or GCP and managed AI/ML services.
• Experience with Docker, basic Kubernetes operations, and CI/CD tools such as GitHub Actions, GitLab CI, or Jenkins.
• Experience building, deploying, monitoring, or troubleshooting ML pipelines and production workflows.
• Strong analytical, problem-solving, debugging, communication, and cross-functional collaboration skills.

Preferred Qualifications

• Experience with monitoring and observability tools such as Prometheus, Grafana, or Datadog.
• Familiarity with experiment tracking and model management tools such as MLflow or Weights & Biases.
• Experience with infrastructure as code and configuration management using Terraform or Ansible.
• Familiarity with feature stores such as Feast or Tecton.
• Experience with internal developer tools, SDKs, and self-service platform capabilities.
• Knowledge of NLP, computer vision, speech processing, reinforcement learning, or Generative AI.
• Experience in government, regulated, or large enterprise environments.

Skills and Competencies

• Strong attention to detail and commitment to operational reliability.
• Ability to independently troubleshoot complex pipelines and distributed systems.
• Clear technical documentation and incident reporting skills.
• Collaborative approach to working with engineers, data scientists, and nontechnical stakeholders.
• Continuous improvement mindset focused on reducing operational issues, deployment delays, and unnecessary cloud costs.

Candidate Submission Requirements
Please include the following details with every candidate submission:

1. Updated resume.
2. Candidate's current location.
3. Work authorization.
4. Availability and potential start date.
5. Rate confirmation.
6. LinkedIn profile.
7. Education and relevant certifications.

EOE