Machine Learning Engineer

Role Summary

Machine Learning Engineer at Elanco focused on the end-to-end lifecycle of custom and third-party ML models, translating complex business problems into scalable, production-ready AI solutions. The role blends software engineering discipline with deep ML expertise to design, build, and deploy models that deliver real-world value. Location: Indianapolis, IN (Hybrid).

Responsibilities

• Custom Model Development: Design, build, and train bespoke ML models tailored to specific business needs, from initial prototype to full implementation.
• Third-Party Model Utilization: Identify, tune and deploy third-party ML models, covering proprietary and open-source models.
• Production Deployment: Manage the deployment of ML models into production environments, ensuring they are scalable, reliable, and performant.
• MLOps and Automation: Build and maintain robust MLOps pipelines for CI/CD, model monitoring, and automated retraining.
• Data Pipeline Construction: Collaborate with data engineers/stewards to build and optimize data pipelines that feed ML models, ensuring data quality and efficient processing for training and inference.
• Cross-Functional Collaboration: Work closely with data scientists, product managers, and software engineers to define requirements, integrate models into applications, and deliver impactful features.
• Code and System Quality: Write clean, maintainable, and well-tested production-grade code. Uphold high software engineering standards across all projects.
• Performance Tuning: Monitor and analyze model performance in production, identifying opportunities for optimization and iteration.

Qualifications

• Required: 3+ years experience in Machine Learning/Engineering or relevant work.
• Required: Advanced proficiency in Python and deep experience with core ML/data science libraries (e.g., PyTorch, TensorFlow, scikit-learn, pandas, NumPy).
• Required: Strong software engineering fundamentals, including data structures, algorithms, testing, and version control (Git).
• Required: Proven hands-on experience deploying machine learning models into a production environment.
• Required: Experience with MLOps tooling and containerization technologies (Docker, Kubernetes).
• Required: Practical experience with public Cloud platforms, specifically Microsoft Azure and Google Cloud Platform (GCP) and their ML services (e.g., Azure ML, Vertex AI).
• Preferred: DevSecOps concepts and tooling, including CI/CD, Git, Docker/Kubernetes, Terraform.
• Preferred: Solid understanding of ML theory (deep learning, NLP, classical ML).
• Preferred: Pragmatic, results-oriented problem solving ability to translate ambiguous requirements into concrete technical solutions.
• Preferred: Industry experience in life sciences, including regulatory/compliance requirements and opportunities for ML applications in life science outcomes.
• Preferred: Excellent communication skills for conveying complex technical decisions to technical and non-technical stakeholders.

Skills

• Python programming; PyTorch; TensorFlow; scikit-learn; pandas; NumPy; Git; Docker; Kubernetes.

Education

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Artificial Intelligence, or related quantitative field.

Additional Requirements

• Travel: Minimal