Machine Learning Engineer

Role Summary

Machine Learning Engineer at Elanco, focusing on end-to-end lifecycle of custom and third-party ML models to translate complex business problems into scalable, production-ready AI solutions. Role blends software engineering discipline with deep ML expertise to design, build, and deploy models delivering real-world value. Four strategic priorities: Pipeline Acceleration, Manufacturing Excellence, Sales Effectiveness, and Productivity.

Responsibilities

• Custom Model Development: Design, build, and train bespoke ML models from prototype to full implementation.
• Third-Party Model Utilization: Identify, tune, and deploy third-party ML models (proprietary and open-source).
• Production Deployment: Manage deployment of ML models into production environments ensuring scalability, reliability, and performance.
• MLOps and Automation: Build/maintain robust CI/CD, model monitoring, and automated retraining pipelines.
• Data Pipeline Construction: Collaborate with data engineers/stewards to create and optimize data pipelines for training and inference.
• Cross-Functional Collaboration: Work with data scientists, product managers, and software engineers to define requirements and deliver features.
• Code and System Quality: Write clean, maintainable, production-grade code with strong software engineering standards.
• Performance Tuning: Monitor/optimize model performance in production and iterate improvements.

Qualifications

• Required: Bachelor's or Master's in Computer Science, Software Engineering, AI, or related quantitative field; 3+ years in ML/engineering; advanced Python proficiency and experience with PyTorch, TensorFlow, scikit-learn, pandas, NumPy; strong software engineering fundamentals (data structures, algorithms, testing, Git); hands-on ML model deployment; experience with MLOps tools and containerization (Docker, Kubernetes); cloud experience with Azure and GCP and their ML services.
• Preferred: DevSecOps experience (CI/CD, Git, Docker/Kubernetes, Terraform); solid ML theory (deep learning, NLP, classical ML); strong problem-solving and communication; industry experience in life sciences and regulatory/compliance awareness.

Skills

• Machine Learning model development and deployment
• MLOps, CI/CD, monitoring, automated retraining
• Data pipeline design and data quality management
• Python programming, ML/data science libraries
• Cloud platforms (Azure, GCP) and ML services
• Software engineering practices and version control

Education

• Bachelor‚Äôs or Master‚Äôs degree in Computer Science, Software Engineering, Artificial Intelligence, or related field

Additional Requirements

• Location: Global Headquarters ‚Äì Indianapolis, IN (Hybrid)
• Travel: Minimal