AI Engineer, Business Operations

Responsibilities

• Productionize AI/ML models into scalable services (e.g., APIs, batch inference, streaming inference) with strong standards for reliability and performance.
• Collaborate with AI Scientists to convert research prototypes into production-ready components (feature computation, preprocessing, post-processing, evaluation loops).
• Integrate models with data pipelines built by Data Engineers and ensure seamless end-to-end flow from raw data to AI-driven output.
• Build and maintain inference pipelines using Python and orchestration frameworks (e.g., Airflow), supporting deployment across cloud and on-prem environments.
• Implement CI/CD, containerization, and automated testing to ensure safe, repeatable, and automated model deployments.
• Establish monitoring and observability for models and services (system metrics, data drift, performance regression, alerting).
• Partner with BizOps and Commercial stakeholders to transform manual workflows into AI-enabled services that improve operational decision-making.
• Optimize end-to-end model serving latency, throughput, and cost using packaging strategies, scaling policies, caching, and parallelization.
• Contribute to documentation, reusable templates, and engineering best practices to accelerate AI adoption across the organization.

Qualifications

• Education: Bachelor’s degree or higher in Computer Science, Engineering, or related technical field.
• Experience: 3+ years of software engineering experience, including building or deploying AI systems in production environments.
• Skills: Strong proficiency in Python for services, pipelines, and ML tooling; Experience deploying AI models in production across on-prem or cloud environments (AWS or Azure); Experience with big-data and orchestration frameworks (e.g., Spark, Airflow) for scalable pipelines; Strong understanding of software engineering best practices including CI/CD, containerization (Docker, Kubernetes), automated testing, and version control; Experience with model optimization techniques such as ONNX / ONNX Runtime, model quantization, or other performance-oriented inference tooling.
• Preferred: Interest or exposure to MLOps concepts (model registries, feature stores, experiment tracking, automated retraining, monitoring); Master’s degree or higher in a relevant field; Experience in regulated industries (e.g., biopharma, healthcare, and finance); A portfolio of launched AI/ML projects or contributions to production of AI systems; Proficiency in SQL and familiarity with modern data warehouses such as Snowflake.

Who Thrives In This Role

• Engineers who enjoy transforming research into resilient, user-facing products.
• Builders who balance rapid iteration with production-grade engineering standards.
• Collaborators who can partner with business teams to convert manual workflows into scalable AI services.
• Pragmatic problem-solvers who can operate autonomously and drive impact in ambiguous, cross-functional settings.