- Company Name
- Elanco
- Job Title
- Machine Learning Engineer
- Job Description
-
Job title: Machine Learning Engineer
Role Summary:
Design, build, and deploy scalable machine‑learning solutions that translate complex business problems into production‑ready AI models. Own the full model lifecycle, from prototype through deployment, monitoring, and iterative improvement, while collaborating cross‑functionally to integrate models into applications and maintain high software quality.
Expectations:
- Deliver reliable, high‑performance ML models that provide measurable business value.
- Rigorously test and validate models, ensuring data integrity and compliance with security best practices.
- Continuously improve model performance through monitoring, feedback loops, and automated retraining.
- Communicate technical concepts clearly to non‑technical stakeholders.
Key Responsibilities:
- Design, develop, train, and fine‑tune custom ML models for specific business use cases.
- Evaluate, adapt, and deploy third‑party and open‑source models with minimal friction.
- Build and maintain MLOps pipelines (CI/CD, monitoring, model registry, automated retraining).
- Collaborate with data engineers to construct high‑quality data pipelines for training and inference.
- Integrate models into production systems, ensuring scalability, reliability, and performance.
- Write clean, well‑tested, production‑grade code, following best practices in version control and testing.
- Monitor deployed models, analyze performance metrics, and identify optimization opportunities.
- Work closely with data scientists, product managers, and software engineers to define requirements and deliver features.
Required Skills:
- Advanced proficiency in Python; strong command of ML/Data libraries (PyTorch, TensorFlow, scikit‑learn, pandas, NumPy).
- Solid software engineering fundamentals: data structures, algorithms, testing, version control (Git).
- Hands‑on experience deploying ML models to production environments.
- MLOps expertise: CI/CD, containerization (Docker, Kubernetes), model monitoring, and automated retraining workflows.
- Practical experience with cloud ML services on Microsoft Azure ML and Google Cloud Vertex AI.
- Familiarity with DevSecOps tools and practices (Git, CI/CD, IaC – Terraform, Docker, Kubernetes).
- Strong knowledge of ML theory covering deep learning, NLP, and classical algorithms.
- Problem‑solving oriented with a focus on delivering results.
Required Education & Certifications:
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, Artificial Intelligence, or a quantitative discipline.
- Certifications in relevant areas (e.g., Azure AI Engineer Associate, GCP Professional Machine Learning Engineer, TensorFlow Developer) are desirable but not mandatory.