MLOps Platform Engineering Senior Manager

Overview

Our client is accelerating its innovation agenda with an expanding portfolio of AI/ML, GenAI, and platform modernization initiatives. We are seeking a MLOps Platform Engineering Senior Manager operate as a Forward Deployed Engineer (FDE)—embedding directly with business stakeholders across multiple domains to build, deploy, and operationalize high-impact, production-ready solutions.

This is a strategic, hands-on role ideal for a highly adaptable technical generalist who thrives in ambiguous environments, works autonomously, and bridges the gap between business needs and platform engineering roadmaps. You will collaborate across key business units including Clinical Development, Tech Ops, AI Labs & Innovation, Research & Discovery, Translational & Quantitative Science (TQS), and more.

• Embed directly with domain teams (Clinical, Research, Commercial, Tech Ops, Medical/Regulatory) to understand workflows, constraints, and success metrics.
• Rapidly prototype, customize, and deploy solutions leveraging AI/ML, GenAI, cloud services, and domain-specific data sources.
• Translate business challenges into actionable technical requirements and influence platform roadmaps.
• Provide hands-on support, training, and technical guidance to business stakeholders.
• Synthesize customer feedback to drive platform improvements and feature prioritization.
• Advocate for customer needs across engineering teams to ensure platform alignment and value delivery.
• Build and deploy models supporting clinical operations, research, commercial analytics, Tech Ops, TQS, and market intelligence.
• Architect and automate AI/ML pipelines using AWS and Git Lab (Sage Maker, Bedrock, Lambda, Step Functions, Redshift, Glue, S3, etc.).
• Integrate LLMs, AI agents, and GenAI solutions into business workflows.
• Ensure solutions meet requirements for scalability, security, Info Sec, and compliance (HIPAA, GDPR, Responsible AI).
• Collaborate with Platform Engineering on MLOps, CI/CD, and Infrastructure as Code (IaC) improvements.
• Implement monitoring, logging, model drift detection, and governance.
• Optimize workloads via distributed computing, GPU acceleration, or serverless architectures.
• Work with diverse datasets across clinical, commercial, research, and real-world evidence sources.
• Enable seamless data ingestion, access, and transformation across cloud storage, data lakes, and AI-driven applications.
• Partner with cross-functional teams—data scientists, analysts, engineering, and business users—to ensure solutions deliver measurable business impact.
• Serve as the bridge between platform teams (hub) and business units (spokes).
• Provide technical mentorship and ensure best practices in solution delivery.
• Drive operationalization of insights at scale.
• Stay current with advancements in AI/ML, GenAI, cloud computing, and Dev Ops.
• Experiment with cutting-edge technologies such as LLMs, AI agents, multimodal AI, AutoML, and RAG systems.
• Champion data-driven decision-making and AI-first approaches across the organization.

• Master’s or Ph.D. in Computer Science, Engineering, Mathematics, Physics, Chemistry, Statistics, or a related field.
• 4+ years of experience in AI/ML, cloud engineering, data science, infrastructure, MLOps, and/or Dev Ops.
• Experience working directly with stakeholders to deliver technical solutions.
• Proven history of deploying production systems that deliver measurable value.
• Experience in healthcare, biotech, or life sciences is a bonus.
• Broad experience across AWS cloud services.
• Expertise in AI/ML and GenAI frameworks (PyTorch, Tensor Flow, Scikit-Learn, Hugging Face).
• Familiarity with data platforms such as Databricks, Snowflake, Lake Formation, or dbt.
• Demonstrated success building and scaling AI-powered applications.
• Knowledge of Generative AI, AI agents, LLMs, and NLP solutions.
• Strong IaC experience (Terraform, Open Tofu, CDK, Pulumi).
• Deep understanding of containerization (Docker, Podman) and orchestration (Kubernetes, Rancher).
• Experience with large-scale CPU/GPU/multi-GPU compute environments (CUDA fundamentals a plus).
• Understanding of life sciences operations (clinical, sales, marketing, research, commercial, market…