AI/ML Engineer III designing and architecting AI solutions at Hewlett Packard Enterprise. Collaborating with teams to drive innovation and tackle complex problems.
About the role
- Senior ML Platform Engineer at GEICO focusing on building scalable machine learning infrastructure and managing AI applications. Responsible for design, implementation, and mentoring within the ML team.
Responsibilities
- Design and implement scalable infrastructure for training, fine-tuning, and serving open source LLMs (Llama, Mistral, Gemma, etc.)
- Architect and manage Kubernetes clusters for ML workloads, including GPU scheduling, autoscaling, and resource optimization
- Design, implement, and maintain feature stores for ML model training and inference pipelines
- Build and optimize LLM inference systems using frameworks like vLLM, TensorRT-LLM, and custom serving solutions
- Ensure 99.9%+ uptime for ML platforms through robust monitoring, alerting, and incident response procedures
- Design and implement ML platforms using DataRobot, Azure Machine Learning, Azure Kubernetes Service (AKS), and Azure Container Instances
- Develop and maintain infrastructure using Terraform, ARM templates, and Azure DevOps
- Implement cost-effective solutions for GPU compute, storage, and networking across Azure regions
- Ensure ML platforms meet enterprise security standards and regulatory compliance requirements
- Evaluate and potentially implement hybrid cloud solutions with AWS/GCP as backup or specialized use cases
- Mentor junior engineers and data scientists on platform best practices, infrastructure design, and ML operations
- Lead comprehensive code reviews focusing on scalability, reliability, security, and maintainability
- Design and deliver technical onboarding programs for new team members joining the ML platform team
- Work closely with data scientists to understand requirements and optimize workflows for model development and deployment
Requirements
- Bachelor’s degree in computer science, Engineering, or related technical field (or equivalent experience)
- 8+ years of software engineering experience with focus on infrastructure, platform engineering, or MLOps
- 3+ years of hands-on experience with machine learning infrastructure and deployment at scale
- 2+ years of experience working with Large Language Models and transformer architectures
- Proficient in Python; strong skills in Go, Rust, or Java preferred
- Proven experience working with open source LLMs (Llama 2/3, Qwen, Mistral, Gemma, Code Llama, etc.)
- Proficient in Kubernetes including custom operators, helm charts, and GPU scheduling
- Deep expertise in Azure services (AKS, Azure ML, Container Registry, Storage, Networking)
- Experience implementing and operating feature stores (Chronon, Feast, Tecton, Azure ML Feature Store, or custom solutions)
- Hands-on experience with inference optimization using vLLM, TensorRT-LLM, Triton Inference Server, or similar
- Advanced experience with Azure DevOps, GitHub Actions, Jenkins, or similar CI/CD platforms
- Proficiency with Terraform, ARM templates, Pulumi, or CloudFormation
- Deep understanding of Docker, container optimization, and multi-stage builds
- Experience with Prometheus, Grafana, ELK stack, Azure Monitor, and distributed tracing
- Knowledge of both SQL and NoSQL databases, data warehousing, and vector databases
- Demonstrated track record of mentoring engineers and leading technical initiatives
- Experience leading design reviews with focus on compliance, performance, and reliability
Benefits
- Comprehensive Total Rewards program that offers personalized coverage tailor-made for you and your family’s overall well-being.
- Financial benefits including market-competitive compensation; a 401K savings plan vested from day one that offers a 6% match; performance and recognition-based incentives; and tuition assistance.
- Access to additional benefits like mental healthcare as well as fertility and adoption assistance.
- Supports flexibility- We provide workplace flexibility as well as our GEICO Flex program, which offers the ability to work from anywhere in the US for up to four weeks per year.
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