Best Egg

Senior Software Engineer, ML Operations

Job Description

Data is at the heart of everything we do. Join a Data & MLOps team working at the cutting edge within our industry and constantly advancing our ML/AI practice. Working with dedicated Data Science partners, we are creating machine-learning models that power innovation and creative insights, pioneering new products, and helping our business reach the next level. We work on diverse projects across all business units within the company and have a direct engagement model between data engineers, data scientists, software engineers, and business stakeholders.

Join a collaborative group of Data Scientists skilled in predictive analytics and help them deploy and monitor real-time production-grade models in projects spanning the business—from credit risk and direct marketing targeting to fraud operations and beyond! Enjoy the stability of a profitable, award-winning Fintech and challenge yourself with plenty of growth and upward mobility within a data-rich environment. Be a part of a growing team using the latest tools and technologies to disrupt the industry and empower our customers to reach their financial goals.


Role Highlights
  • 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.

    • To Be Successful in This Role
  • 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.

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