Senior ML Engineer, Commercial Delivery

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 AIdriven 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 hubandspoke 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 cloudbased ML infrastructure while ensuring models are optimized governed and deployed at scale.

This is a highexpectation highimpact role that requires expertise in cuttingedge AI cloud computing MLOps and software engineering to drive the next generation of commercial datadriven decisionmaking.

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 realworld 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 NLPbased 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 realworld evidence (RWE) IQVIA Symphony and other biopharma sources.

• Enable seamless data integration and processing across cloud storage data lakes and AIdriven commercial applications.

• Work closely with commercial teams business analysts and data scientists to ensure AI models deliver businessrelevant highimpact insights.

• Act as the bridge between centralized AI/ML teams (hub) and commercial stakeholders (spokes) ensuring alignment performance and business impact.

• Drive crossfunctional collaboration ensuring AIdriven 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 multimodal AI AutoML RAGbased systems etc.) to enhance commercial strategy.

• Champion a datadriven culture advocating for AIfirst approaches in commercial decisionmaking.

Required Qualifications & Experience:

• Masters 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 ScikitLearn Hugging Face Transformers.

• Strong programming skills in Python SQL and infrastructure as code (Terraform CloudFormation or CDK).

• Experience in building and scaling AIpowered 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 finetuning and NLPbased 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.

• Selfdriven 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 hubandspoke model balancing centralized AI/ML initiatives with business unit needs.