Xenon Seven

Machine Learning Engineer

• Design, deploy, and maintain production-grade MLOps pipelines and infrastructure for continuous model training, deployment, versioning, and monitoring
• Implement automated model drift detection, performance monitoring, and self-healing inference pipelines
• Operationalize and integrate production ML models into operational technology, API manufacturing workflows, and chemical process control systems
• Deploy predictive models for batch processing, process control optimization, real-time quality assurance, and facility automation
• Build low-latency, high-throughput microservices and serving architectures using FastAPI, Triton Inference Server, and TorchServe
• Containerize and orchestrate ML workloads across distributed cloud and edge systems using Kubernetes, Docker, Kubeflow, and MLflow
• Partner with chemical engineers, computational biologists, and software architects to translate operational needs into production ML solutions
• Establish enterprise MLOps standards, model governance, and CI/CD best practices
• Own architectural design and hands-on execution of production ML systems, model integration APIs, and scalable MLOps pipelines
• Collaborate onsite with process engineers, life science researchers, and platform engineering teams

Senior Machine Learning Engineer

Data Engineer

• Design, build, and maintain production-grade data pipelines and architecture for scientific, clinical trial, and research informatics data
• Structure complex, multi-modal clinical and scientific datasets for advanced analytics, enterprise reporting, and downstream machine learning models
• Ingest, harmonize, and model operational technology and manufacturing process datasets, including API manufacturing pipelines, batch processing data, MES, SCADA, and OSIsoft PI systems
• Unify laboratory and facility data pipelines into centralized, highly available enterprise data platforms
• Build robust ETL/ELT pipelines using Databricks, Snowflake, AWS/Azure, PySpark, and SQL
• Ensure pipelines and platform integrations adhere to enterprise governance, data residency, and GxP regulatory standards
• Partner with process engineers, chemical engineering leads, and research informatics directors to translate operational needs into technical specifications
• Establish engineering best practices, data modeling standards, and pipeline monitoring frameworks across the enterprise data stack
• Own the architectural vision and hands-on execution of scalable data platforms and pipelines
• Collaborate with process engineers, clinical scientists, and platform teams to build data infrastructure supporting drug discovery and manufacturing operations

Senior Data Engineer

Artificial Intelligence

• Design and build AI-ready data products on Snowflake and/or Databricks
• Implement semantic layers and governed datasets for traditional BI and natural-language querying
• Deploy and operate Snowflake Cortex capabilities and/or Databricks Genie spaces with Unity Catalog, tuning for accuracy, adoption, and business relevance
• Build RAG pipelines and conversational analytics applications grounded in governed enterprise data
• Build Streamlit or Databricks Apps that enable business users to query data without writing SQL
• Engineer ETL/ELT pipelines using dbt, Airflow, Snowpark, and PySpark
• Implement data governance, including RBAC, row/column-level security, masking, lineage, auditability, catalog, and metadata management, in a regulated pharma environment
• Optimize data-platform cost and performance through warehouse sizing, cluster tuning, and query optimization
• Optimize AI cost and performance through token usage management, caching, and model routing
• Partner with Finance stakeholders to translate domain requirements into semantic models and governed data products

AI Data Enablement Engineer

DevOps Engineer

• Own the full lifecycle of AI solutions for Finance business units, from discovery with CFO stakeholders through production deployment, user adoption, and ROI measurement
• Rapidly prototype vertical applications using Streamlit, Databricks Apps, FastAPI/Flask, or lightweight React interfaces within 1–2 weeks
• Handle edge cases, data quirks, business-rule exceptions, and user training to turn prototypes into enterprise products
• Build production-grade vertical AI tools using Snowflake Cortex or Databricks Genie
• Construct RAG pipelines with advanced chunking, vector databases, retrieval evaluation, grounding, and citation
• Implement LLM orchestration with LangChain, LangGraph, and LlamaIndex, including prompt engineering and hallucination/accuracy evaluation
• Deploy applications in regulated enterprise environments while adhering to RBAC, data residency, audit, SOX, and GxP constraints
• Connect solutions to governed datasets, semantic models, and enterprise platforms such as SAP, Workday, and Coupa via secure REST APIs
• Instrument applications with usage metrics, telemetry, and business-outcome tracking
• Advise CFO office, FP&A, and Controllership leaders by translating ambiguous operational friction into technical roadmaps
• Extract reusable code components, prompt templates, evaluation harnesses, and retrieval patterns into shared enterprise assets

Senior Machine Learning Engineer

Senior Data Engineer

AI Data Enablement Engineer

Forward Deployment Engineer