Machine Learning Engineer

• Develop and implement advanced ML and DL models
• Apply NLP techniques for OCR-based text extraction, document classification, and document summarization
• Work on large language models (LLMs) and Gen-AI projects
• Utilize AWS SageMaker and Databricks for model development and deployment
• Conduct data pre-processing, exploratory data analysis (EDA), and model evaluation
• Collaborate with cross-functional teams to integrate AI/ML solutions into business processes

AI/ML Engineer

• Build and operate scalable pipelines and interfaces delivering model-ready data to ML, generative AI, and agentic workflows
• Enable closed-loop scientific learning by capturing, governing, and exposing newly generated scientific data to downstream modeling, evaluation, and agentic workflows
• Establish model deployment, serving, monitoring, access management, and lifecycle management across development and production environments
• Implement model, data, and workflow versioning with reproducible releases, rollback capabilities, and lifecycle traceability
• Establish monitoring, observability, alerting, and performance management for ML workflows, deployed models, and AI services
• Develop and maintain automated workflows for testing, release management, environment management, and operational excellence
• Scale AI capabilities for growing scientific data volumes, computational demands, and autonomous discovery workflows
• Monitor production data quality, model performance, drift, latency, reliability, and resource utilization
• Partner with data scientists, technology teams, and domain experts on integrations between scientific data products and AI/ML workflows
• Enable reproducible training, fine-tuning, evaluation, experimentation, and deployment capabilities
• Establish reusable patterns, best practices, and standards for production deployment
• Contribute to security, access control, AI governance, documentation, and cost management

Senior Machine Learning Engineer, Biologics Discovery

• Build and operate scalable pipelines and interfaces that deliver model-ready data to ML, generative AI, and agentic workflows
• Enable closed-loop scientific learning by capturing, governing, and making newly generated scientific data available to downstream modeling, evaluation, and agentic workflows
• Establish operational capabilities for model deployment, serving, monitoring, access management, and lifecycle management across development and production environments
• Implement model, data, and workflow versioning with reproducible releases, rollback capabilities, and lifecycle traceability
• Establish monitoring, observability, alerting, and performance management practices for ML workflows, deployed models, and AI services
• Develop and maintain automated workflows for testing, release management, environment management, and operational excellence
• Enable AI capabilities to scale with scientific data volumes, computational demands, and autonomous discovery workflows
• Monitor production model and system behavior, including data quality, model performance, drift, latency, reliability, and resource utilization
• Partner with data scientists, technology teams, and domain experts to establish integration patterns between scientific data products and AI/ML workflows
• Enable reproducible training, fine-tuning, evaluation, experimentation, and deployment capabilities
• Establish reusable patterns, best practices, and standards for transitioning experimentation to production
• Contribute to security, access control, AI governance, documentation, and cost management practices