Take ownership of an ML deployment system spanning multiple production environments and continue to research efficient and effective strategies.
Improve, expand, and streamline our existing deployment pipelines to support faster deployments and automated model retraining.
Collaborate with Data Scientists to understand model requirements and provide guidance to ensure seamless integration with production environments.
Develop automations that empower data scientists to self-serve, remove manual steps from our processes, and streamline their training workflows.
Build and maintain production-level inference environments, including low-latency real-time APIs and batch predictions, and monitor these environments to ensure uptime, resiliency, and latency SLAs are met.
Work with modern CI/CD tools to deploy ML/AI models at scale in a production setting.
Drive the deployment and optimization of custom AI and LLM models, supporting data scientists and AI engineers in fine-tuning, evaluating, and serving large language models for real-world use cases.
Contribute to the infrastructure, pipelines, and monitoring needed for generative AI systems, including vector databases, prompt orchestration frameworks, and scalable inference services.
Enjoy a great company culture rich in collaboration, teamwork, no politics, learning, and frequent wins.

Requirements

At least five (5) years of professional engineering experience or work program equivalents in a relevant field.
Experience in operationalization of Data Science projects (MLOps) on AWS; specific experience with EKS, Lambda, Step Functions, and SageMaker.
Experience designing, building, and operating container-based cloud infrastructure with Terraform and other infrastructure-as-code tools in a production setting.
Experience in CI/CD pipeline implementation; experience with ArgoCD, Argo Workflows, and GitHub Actions a plus.
Proficiency in Python for both ML and general software engineering tasks; good knowledge of Bash and Unix command line tools.
Extensive knowledge of the machine learning development lifecycle and associated tooling; demonstrated experience with Metaflow, Flyte, Kubeflow, etc.
Demonstrated experience building production-grade, RESTful APIs for ML products; experience building data scientist tooling a plus.
Hands-on experience with AI model development, fine-tuning, or deployment—particularly with large language models (e.g., OpenAI, Anthropic, Hugging Face, or custom transformer-based models).
Knowledge of modern AI infrastructure tools such as vector databases (e.g., Pinecone, FAISS, or Weaviate), model-serving platforms, and prompt management frameworks.
Ability to work in a fast-paced environment and strong technical communication skills.
Enjoy a culture rich in direct communication, no politics, and continual learning—where we celebrate success and have fun too.

Benefits

Pre-tax and post-tax retirement savings plans with a competitive company matching program
Generous paid time-off plans including vacation, personal/sick time, paid short-term and long-term disability leaves, paid parental leave, and paid company holidays
Multiple health care plans to choose from, including dental and vision options
Flexible Spending Plans for Health Care, Dependent Care, and Health Reimbursement Accounts
Company-paid benefits such as life insurance, wellness platforms, employee assistance programs, and Health Advocate programs
Other great discounted benefits include identity theft protection, pet insurance, fitness center reimbursements, and many more!

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

MLOpsPythonBashUnix command lineCI/CDRESTful APIsAI model developmentlarge language modelsmachine learning development lifecycleinfrastructure-as-code

Soft skills

collaborationteamworktechnical communicationfast-paced environmentlearningproblem-solvingownershipguidancestreamlining processesempowerment