AI Engineer

Responsibilities

• Design, develop, and deploy machine learning and generative AI models aligned with CRG Digital priorities, including predictive analytics, NLP/text solutions, automation, and decision-support capabilities.
• Apply statistics, programming, data modeling, simulation, and advanced mathematics to address business challenges.
• Evaluate model performance with appropriate validation metrics and techniques, then optimize accordingly.
• Build reusable pipelines, APIs, and components to enable scalable AI product delivery.
• Preprocess, clean, transform, and integrate structured and unstructured data from clinical and operational sources.
• Develop and maintain ETL and data workflows that support ML and generative AI workloads.
• Work with relational, document, columnar, graph, and object stores; design schemas for AI and analytics use cases.
• Uphold data quality, reproducibility, data lineage, and governance compliance.
• Collaborate with software engineering and platform teams to integrate AI models into production systems with monitoring and observability.
• Support documentation, traceability, and regulatory readiness, including auditability and GxP considerations.
• Contribute to CRG Digital product development lifecycle across discovery, prototyping, testing, deployment, and iteration.
• Stay current on emerging AI and ML technologies and evaluate opportunities for CRG Digital roadmaps.
• Foster an innovative ecosystem through collaboration with external partners and technology collaborators.
• Help advance internal digital and AI upskilling via documentation, best practices, and knowledge sharing.

Requirements

• Bachelor’s degree or equivalent; Master’s degree preferred.
• Minimum 2 years of experience in machine learning, AI model development, or data science.
• Educational equivalency or related experience may be considered in lieu of formal degrees.
• Proficiency with SQL, Python, Spark, and common AI/ML libraries and packages.
• Hands-on experience with generative AI, large language models, NLP, prompt engineering, and LLMOps best practices.
• Strong exploratory data analysis skills, including validating, visualizing, and communicating model behavior.
• Experience with cloud architecture, distributed computing, and modern ML platforms.
• Familiarity with data governance, security, and regulatory compliance, preferably in regulated or clinical settings.
• Ability to juggle multiple priorities in a matrixed environment.
• Strong analytical thinking, problem-solving, and communication abilities.
• Capable of working independently and collaborating effectively with cross-functional teams.
• Must be legally authorized to work in the United States or Mexico without sponsorship.
• Must pass a comprehensive background check, including a drug screening.
• Excellent communication with diverse groups and clear information exchange.
• Ability to work in standard office environments, maintaining typical working hours.
• Proficiency with standard office equipment and collaboration tools.
• Comfort handling pressure while managing multiple projects; may require travel 0-20% as needed.

Technologies

• Python, SQL, Spark
• TensorFlow, PyTorch, Keras, scikit-learn
• LangChain, LangGraph, OpenAI/LLM SDKs
• Pandas, NumPy, Jupyter
• Databricks, AWS, Azure, GCP; S3, Azure Blob Storage
• Git/GitHub

Benefits

• Eligible for a variable annual bonus