ML Engineer

Overview

Hybrid
Depends on Experience
Contract - W2
Contract - Independent
Contract - 12 Month(s)
No Travel Required

Skills

AI/ML
Commercial Delivery
MLOps
AWS
Python
sql
Terraform
CloudFormation
cdk
Generative AI
LLM fine-tuning
Docker

Job Details

Senior ML Engineer, Commercial Delivery

Location: Princeton, New Jersey

Hybrid-Long term

Role Overview:

As we continue to push the boundaries of innovation, we seek a Commercial Delivery Engineer to bridge the gap between commercial data science, AI/ML operations, and platform engineering. As a Commercial Delivery Engineer, you will be responsible for delivering and operationalizing AI-driven solutions for commercial strategy, business intelligence, market access, and sales performance. This role requires an elite technical and strategic mindset, working at the intersection of AI/ML, Generative AI, cloud computing, and MLOps.

You will work within a hub-and-spoke model, collaborating with centralized data science platform teams (hub) and commercial business units (spokes) to ensure scalability, reliability, and performance of AI/ML models in production. You will also partner with platform engineering teams to build and enhance cloud-based ML infrastructure while ensuring models are optimized, governed, and deployed at scale.

This is a high-expectation, high-impact role that requires expertise in cutting-edge AI, cloud computing, MLOps, and software engineering to drive the next generation of commercial data-driven decision-making.

This role is based out of our Princeton office and requires for you to be on site 60% of the time

Key Responsibilities:

  • Build and deploy AI/ML models that support commercial analytics, customer segmentation, forecasting, market intelligence, and real-world data insights.
  • Architect, automate, and optimize AI pipelines on AWS (SageMaker, Bedrock, Lambda, Step Functions, Redshift, Glue, S3, etc.).
  • Integrate Generative AI and LLMs into commercial workflows, enabling NLP-based insights, sales intelligence, and customer engagement strategies.
  • Ensure AI solutions are scalable, robust, and meet compliance standards (HIPAA, GDPR, responsible AI guidelines).
  • Partner with platform engineering teams to build and enhance MLOps, CI/CD, and infrastructure as code (IaC) pipelines for AI models.
  • Implement model monitoring, logging, drift detection, and governance to ensure continuous improvement and compliance.
  • Optimize AI workloads using distributed computing, GPU acceleration, serverless architectures, and edge AI.
  • Design and optimize data pipelines that connect commercial datasets from EHRs, claims data, real-world evidence (RWE), IQVIA, Symphony, and other biopharma sources.
  • Enable seamless data integration and processing across cloud storage, data lakes, and AI-driven commercial applications.
  • Work closely with commercial teams, business analysts, and data scientists to ensure AI models deliver business-relevant, high-impact insights.
  • Act as the bridge between centralized AI/ML teams (hub) and commercial stakeholders (spokes), ensuring alignment, performance, and business impact.
  • Drive cross-functional collaboration, ensuring AI-driven insights are actionable and operationalized at scale.
  • Provide technical mentorship and leadership in AI/ML delivery, ensuring best practices are followed.
  • Stay ahead of the latest advancements in AI/ML, Generative AI, cloud computing, and commercial analytics.
  • Experiment with emerging AI/ML technologies (LLMs, multi-modal AI, AutoML, RAG-based systems, etc.) to enhance commercial strategy.
  • Champion a data-driven culture, advocating for AI-first approaches in commercial decision-making.

Required Qualifications & Experience:

  • Master's or PhD degree in Computer Science, Physics, Chemistry, Statistics, or a related field
  • 6+ years of experience in AI/ML, cloud engineering, data science, or MLOps within healthcare, biotech, or life sciences.
  • Expert knowledge of AWS cloud services, including SageMaker, Bedrock, Lambda, Step Functions, Redshift, Glue, DynamoDB.
  • Deep expertise in AI/ML frameworks such as PyTorch, TensorFlow, Scikit-Learn, Hugging Face Transformers.
  • Strong programming skills in Python, SQL, and infrastructure as code (Terraform, CloudFormation, or CDK).
  • Experience in building and scaling AI-powered commercial applications (forecasting, NLP, customer intelligence, sales analytics).
  • Proven experience in MLOps, including model deployment, versioning, monitoring, and drift detection.
  • Strong knowledge of Generative AI, LLM fine-tuning, and NLP-based solutions for commercial use cases.
  • Experience with Docker and containerized AI model deployment.
  • Strong understanding of commercial business operations, including sales, marketing, market access, and revenue analytics.
  • High ethical standards, ensuring AI is used responsibly in commercial applications.
  • Self-driven, proactive, and able to lead AI/ML delivery independently.

Nice to haves:

  • Understanding of regulatory requirements for AI in healthcare (HIPAA, GDPR, responsible AI standards).
  • Experience working in a hub-and-spoke model, balancing centralized AI/ML initiatives with business unit needs.
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