At Elanco, the business and technical problem is to translate complex business problems into scalable, production-ready AI solutions to accelerate pipeline, improve manufacturing, enhance sales effectiveness, and increase productivity.
Requirements
- Advanced proficiency in Python and deep experience with core ML/data science libraries (e.g., PyTorch, TensorFlow, scikit-learn, pandas, NumPy).
- Strong foundation in software engineering principles, including data structures, algorithms, testing, and version control (Git).
- Proven, hands-on experience deploying machine learning models into a production environment.
- Experience with MLOps tools and frameworks and containerization technologies (Docker, Kubernetes).
- Practical experience with Public Cloud, specifically Microsoft Azure and Google Cloud Platform (GCP) and their ML services (e.g., Azure ML, Vertex AI).
- Proven experience with relevant DevSecOps concepts and tooling, including Continuous Integration/Continuous Delivery (CI/CD), Git SCM, Containerization (Docker, Kubernetes), Infrastructure-as-Code (HashiCorp Terraform).
- Solid understanding of the theoretical foundations of machine learning algorithms, including deep learning, NLP, and classical ML.
Responsibilities
- Design, build, and train bespoke ML models tailored to specific business needs, from initial prototype to full implementation.
- Identify, tune and deploy third-party ML models, covering proprietary and open-source models.
- Manage the deployment of ML models into our production environments, ensuring they are scalable, reliable, and performant.
- Build and maintain robust MLOps pipelines for Continuous Integration/Continuous Delivery (CI/CD), model monitoring, and automated retraining.
- Collaborate with data engineers/stewards to build and optimize data pipelines that feed ML models, ensuring data quality and efficient processing for both training and inference.
- Write clean, maintainable, and well-tested production-grade code.
- Monitor and analyze model performance in production, identifying opportunities for optimization and iteration.
Other
- A Bachelor’s or Master’s degree in Computer Science, Software Engineering, Artificial Intelligence, or a related quantitative field.
- 3+ years experience in Machine Learning/Engineer or relevant work.
- Work closely with data scientists, product managers, and software engineers to define requirements, integrate models into applications, and deliver impactful features.
- A pragmatic and results-oriented approach to problem-solving, with the ability to translate ambiguous requirements into concrete technical solutions.
- Excellent communication skills, capable of articulating complex technical decisions and outcomes to both technical and non-technical stakeholders.
