Job Description
We're looking for an AI/MLOps Engineer who can own both the AI engineering and operational side of machine learning solutions. This person will be responsible for building, deploying, monitoring, and supporting AI agents, LLM applications, and machine learning models in production. They will work closely with data scientists, AI architects, and engineering teams to ensure AI solutions are scalable, secure, reliable, and easy to maintain.
This individual should have experience taking AI and ML solutions from development through production, including deployment automation, infrastructure management, model monitoring, CI/CD pipelines, and troubleshooting production environments. They should also be comfortable supporting data pipelines, cloud infrastructure, and the operational requirements of large-scale AI applications.
Day-to-Day:
Build, deploy, and support AI agents, LLM applications, and machine learning models in production.
Design and maintain MLOps and AgentOps processes including CI/CD pipelines, automated deployments, model versioning, monitoring, and retraining workflows.
Manage and optimize GCP infrastructure supporting AI and machine learning workloads.
Monitor application performance, system health, and model performance while proactively identifying and resolving issues.
Troubleshoot production incidents, perform root cause analysis, and implement long-term solutions.
Develop automation scripts and infrastructure-as-code solutions to improve scalability and operational efficiency.
Partner with data engineering teams to support data pipelines, ETL processes, data quality, and model lifecycle management.
Implement monitoring, logging, alerting, security, and governance best practices across AI platforms.
Collaborate with cross-functional teams to improve the reliability, scalability, and performance of AI and machine learning systems.
We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.
Required Skills & Experience
4+ years of experience in AI Engineering, MLOps, ML Engineering, DevOps, or a related field.
Hands-on experience deploying and supporting machine learning models, AI applications, AI agents, or LLM-based solutions in production.
Strong experience with Google Cloud Platform (GCP), including Vertex AI. This is a required skill.
Strong Python development experience.
Experience building and maintaining CI/CD pipelines using tools such as GitHub Actions, GitLab, Jenkins, or Bitbucket Pipelines.
Experience with Docker and Kubernetes.
Experience with infrastructure-as-code tools such as Terraform or Ansible.
Experience with monitoring and observability tools such as Datadog, Prometheus, Grafana, Cloud Monitoring, or similar platforms.
Strong understanding of machine learning lifecycle management, model deployment, model monitoring, and operational best practices.
Experience supporting data pipelines, ETL processes, and data-intensive applications.
Ability to troubleshoot complex production issues across infrastructure, machine learning models, and applications.
Experience working closely with data scientists, engineers, and business stakeholders to move AI solutions into production.
Nice to Have Skills & Experience
Experience building or supporting AI agents, agentic AI solutions, or enterprise LLM applications.
Deep expertise with Vertex AI and the broader GCP AI ecosystem.
Experience with TensorFlow, PyTorch, or scikit-learn.
Experience with FastAPI or Flask.
Experience with vector databases, RAG architectures, and LLM orchestration frameworks.
Experience with Airflow, Kubeflow, or ML orchestration platforms.
Google Cloud Professional Machine Learning Engineer certification.
Experience mentoring engineers or helping establish MLOps and AI operational best practices.
Benefit packages for this role will start on the 1st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.