Senior AI/ML Engineer (P4369)

• Build core capabilities on medium to large foundation model projects that can span months, including pre-training, fine-tuning, and optimization of SLMs/LLMs/embedding models.
• Leverage known patterns, frameworks, and tools for automating & deploying foundation model solutions using Triton/vLLM serving infrastructure
• Develop new tools, processes and operational capabilities to monitor and analyze foundation model performance, inference latency, throughput, and data accuracy in production environments
• Work with researchers to optimize and scale foundation model training and inference using distributed GPU clusters and best practices in DevOps & MLOps
• Abstract foundation model solutions as reusable packages, APIs, or components that can be deployed across GCP and Azure cloud environments
• Build, steward, and maintain production-grade solutions (robust, reliable, maintainable, observable, scalable, performant) to manage and serve foundation models at enterprise scale
• Research state-of-the-art foundation model architectures, training techniques, fine-tuning methodologies, and serving optimizations to identify new opportunities for implementation across the enterprise
• Understand business requirements and trade-off model size, inference cost, latency, and accuracy to maximize value and translate research into consumable foundation model products or services
• Reduce time to delivery through automated model training pipelines, continuous integration for model updates, and implement continuous monitoring of model performance and drift
• Apply appropriate documentation, version control, and other internal communication practices for foundation model development lifecycle
• Make time-sensitive decisions regarding model deployment, scaling, and performance optimization and escalate urgent production issues to leads and people leaders

• Bachelor's degree or higher in Machine Learning, Computer Science, Computer Engineering, Applied Statistics, or related field.
• 2-3 years of experience developing cloud-based software solutions and an understanding of design for scalability, performance, and reliability.
• 2-3 years of experience using advanced algorithms, programming languages, or technologies
• 2+ yrs hands-on experience building large-scale foundation models (SLMs/LLMs/embeddings), preferably as a data scientist;
• 2+ years of experience in foundation model development preferred
• 2+ years of experience in tech consulting, retail or related professional services preferred
• Hands-on experience in the full end to end SDLC developing foundation model solutions that scale and leverage CI/CD and MLOps to develop, test, and deploy.
• Experience building large-scale foundation model solutions that have been successfully delivered to stakeholders.
• Excellent communication skills, particularly on technical topics. Strong time and project management skills; the ability to balance multiple, simultaneous work items and prioritize as necessary.
• Knowledge of transformer architectures and foundation model training methods is highly preferred.
• Working experience with PyTorch framework and open-source LLM fine-tuning techniques (LoRA, QLoRA, RLHF)
• Knowledge of E2E foundation model pipeline and MLOps tools (e.g. Model registry, Experiment tracking, feature store, model monitoring)
• Hands-on experience with technologies such as GCP, Azure, Triton Inference Server, vLLM, Databricks, and vector databases
• Strong skills in Python
• Experience with distributed GPU clusters for model training and inference
• Kubernetes & Docker experience
• CI/CD Pipeline and Terraform experience; Github Actions a plus
• API development experience a plus

• The stated salary range represents the entire span applicable across all geographic markets from lowest to highest. Actual salary offers will be determined by multiple factors including but not limited to geographic location, relevant experience, knowledge, skills, other job-related qualifications, and alignment with market data and cost of labor. In addition to salary, this position is also eligible for variable compensation.
• Below is a list of some of the benefits we offer our associates:

• Health: Medical: with competitive plan designs and support for self-care, wellness and mental health. Dental: with in-network and out-of-network benefit. Vision: with in-network and out-of-network benefit.
• Wealth: 401(k) with Roth option and matching contribution. Health Savings Account with matching contribution (requires participation in qualifying medical plan). AD&D and supplemental insurance options to help ensure additional protection for you.
• Happiness: Hybrid work environment. Paid time off with flexibility to meet your life needs, including 5 weeks of vacation time, 7 health and wellness days, 3 floating holidays, as well as 6 company-paid holidays per year. Paid leave for maternity, paternity and family care instances.

84.51°