AI Engineer, Business Operations

Overview

The AI Engineer, Biz Ops will build the AI‑powered services that form the backbone of our decision‑intelligence platform. In this role, you will take AI models developed by AI Scientists and transform them into scalable, production‑ready applications by designing inference pipelines, APIs, and supporting data flows.

You will work closely with Data Engineers to integrate model pipelines with the broader data ecosystem and collaborate with business operations and commercial teams to convert manual, step-driven workflows into AI‑native services. This includes building reliable batch and real‑time inference systems that generate measurable impact across business operations—not limited to any specific domain.

This is a high‑impact role for engineers who enjoy turning research into products, hardening systems for real‑world use, and building the engineering layer that enables AI to operate at scale. While not required, an interest in or exposure to MLOps practices is strongly preferred.

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.

Strongly 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.