MLOps Platform Engineering Senior Manager - Princeton

𝗝𝗼𝗯 𝗧𝗶𝘁𝗹𝗲: · MLOps Platform Engineering Senior Manager · 𝗝𝗼𝗯 𝗧𝘆𝗽𝗲: · Full-Time · 𝗝𝗼𝗯 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻: · Hybrid (60% Onsite) · 𝗝𝗼𝗯 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻: · Permanent · 𝗧𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 · Our client is accelerating its innovation agenda with an expanding portfolio of AI/ML, GenAI, and platform modernizat ...

Job description

𝗝𝗼𝗯 𝗧𝗶𝘁𝗹𝗲:
MLOps Platform Engineering Senior Manager

𝗝𝗼𝗯 𝗧𝘆𝗽𝗲:
Full-Time


𝗝𝗼𝗯 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻:
Hybrid (60% Onsite)


𝗝𝗼𝗯 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻:
Permanent

𝗧𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲
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, TechOps, AI Labs & Innovation, Research & Discovery, Translational & Quantitative Science (TQS), and more.

𝗥𝗼𝗹𝗲𝘀 & 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀
𝗙𝗼𝗿𝘄𝗮𝗿𝗱 𝗗𝗲𝗽𝗹𝗼𝘆𝗲𝗱 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 & 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁

• Embed directly with domain teams (Clinical, Research, Commercial, TechOps, 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.

𝗔𝗜/𝗠𝗟 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁

• Build and deploy models supporting clinical operations, research, commercial analytics, TechOps, TQS, and market intelligence.
• Architect and automate AI/ML pipelines using AWS and GitLab (SageMaker, Bedrock, Lambda, Step Functions, Redshift, Glue, S3, etc.).
• Integrate LLMs, AI agents, and GenAI solutions into business workflows.
• Infrastructure & MLOps
• Ensure solutions meet requirements for scalability, security, InfoSec, 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.

𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 & 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻

• 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.

𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽

• 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.

𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 & 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴

• Stay current with advancements in AI/ML, GenAI, cloud computing, and DevOps.
• 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.

𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗦𝗸𝗶𝗹𝗹𝘀 & 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀
𝗘𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻

• Master's or Ph.

D. in Computer Science, Engineering, Mathematics, Physics, Chemistry, Statistics, or a related field.

𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲

• 4+ years of experience in AI/ML, cloud engineering, data science, infrastructure, MLOps, and/or DevOps.
• 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.

𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗦𝗸𝗶𝗹𝗹𝘀 (𝗠𝘂𝘀𝘁-𝗛𝗮𝘃𝗲)

• Strong programming skills (Python, R, C/C++, Java, Go, Rust, etc.).
• Broad experience across AWS cloud services.
• Expertise in AI/ML and GenAI frameworks (PyTorch, TensorFlow, Scikit-Learn, Hugging Face).
• Familiarity with data platforms such as Databricks, Snowflake, Lake Formation, or dbt.
• Demonstrated success building and scaling AI-powered applications.

MLOps expertise:
model deployment, versioning, monitoring, drift detection.

• Knowledge of Generative AI, AI agents, LLMs, and NLP solutions.
• Strong IaC experience (Terraform, OpenTofu, 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).

𝗗𝗼𝗺𝗮𝗶𝗻 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲

• Understanding of life sciences operations (clinical, sales, marketing, research, commercial, market access).
• Ability to translate complex business processes into technical solutions.
• Knowledge of data privacy, security, and regulatory requirements in healthcare and life sciences.

𝗦𝗼𝗳𝘁 𝗦𝗸𝗶𝗹𝗹𝘀 & 𝗔𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝘀

• Exceptional customer empathy and ability to advocate for user needs.
• Strong communication skills for both technical and non-technical audiences.
• Entrepreneurial mindset with comfort navigating ambiguity.
• Adaptable, collaborative, and solutions-oriented.
• Committed to ethical and responsible AI practices.
• Resilient and effective in fast-paced, customer-facing environments.

𝗣𝗿𝗲𝗳𝗲𝗿𝗿𝗲𝗱 𝗤𝘂𝗮𝗹𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀

• Knowledge of GxP, HIPAA, GDPR, and responsible AI standards.
• Experience in hub-and-spoke engineering models.
• Strong product thinking and cost optimization discipline in cloud environments.
• Openness to exploring diverse technical ideas, solutions, and stakeholder needs.

𝗔𝗱𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗥𝗼𝗹𝗲 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀

• Manage business IT systems, projects, and processes within assigned areas.
• Provide strategic and tactical inputs to engineering leadership.
• Lead implementation of solutions aligned with IT and engineering strategies.
• Collaborate effectively with IT teams, business units, and external vendors.
• Maintain standardized guidelines and workflow procedures to improve efficiency.
• Build relationships across the engineering and technology community.
• Present emerging technologies to IT and enterprise leadership forums.
• Mentor and guide less experienced team members.