The Software Product Engineering organization (SPE) delivers innovative tech solutions to aid, accelerate, and support work done across Lilly. This role is targeted for a software engineer who enjoys working with a cross-functional team, developing robust code in support of accelerating scientific processes, and thinking innovatively. Help design and create back-end solutions that meet the needs of our scientific business areas.
Key Responsibilities & Objectives:
- Collaborate with cross-functional teams to design, develop, and deploy machine learning models and algorithms in production environments, focusing on AI-driven solutions.
- Assist in data pre-processing, feature engineering, and model evaluation to ensure high-quality data and effective model performance.
- Utilize problem-solving skills to identify and rectify bottlenecks in data pipelines and model performance, ensuring optimal user experiences.
- Design and implement prompt engineering strategies for generative AI models to enhance output quality and relevance.
- Develop and optimize natural language processing (NLP) models and applications, including tasks such as text classification, sentiment analysis, and named entity recognition.
- Maintain existing machine learning applications through debugging, continuous updates, and performance optimization.
- Perform code reviews and engage in pair programming sessions to promote best practices and enhance code quality across the team.
- Document machine learning processes, model architectures, and results for knowledge sharing and compliance purposes.
- Work in an Agile environment to deliver customer value, adapting to changing project needs and priorities.
Basic Requirements & Experience Expectations:
- 5+ years of experience in software development, with at least 3 years focused on machine learning and AI.
- 4-year (bachelors) degree in computer science, data science, software engineering, or a related field.
- Proficiency in Python, with extensive experience in libraries such as TensorFlow, PyTorch, or Scikit-learn.
- Good understanding of machine learning concepts, algorithms, and best practices for model training and evaluation.
- Experience in prompt engineering techniques for generative AI models, including LLMs (Large Language Models).
- Proficiency with model deployment frameworks such as Flask or FastAPI for building APIs around machine learning models.
- Familiarity with data manipulation and analysis using tools like Pandas, NumPy, and SQL.
- Proven experience with cloud platforms, particularly AWS, including services like SageMaker, Lambda, EC2, and RDS.
- Good understanding of data storage solutions like PostgreSQL, DynamoDB, and Redis, with a focus on supporting machine learning workloads.
- Experience with version control systems, preferably Git, for collaborative development.
Additional Skills/Preferences:
- Strong understanding of data visualization tools and techniques to effectively communicate model results and insights.
- Familiarity with MLOps practices and tools for managing machine learning lifecycle and deployment.
- Proficiency in natural language processing (NLP) techniques and frameworks, with experience in applications like text classification, language generation, and sentiment analysis.
- Experience with Docker and containerization techniques for deploying machine learning models.
- Excellent teamwork, self-management, and problem-solving abilities.
- Strong communication skills, both oral and written, with the ability to convey complex technical concepts to non-technical stakeholders.
- Experience working in Agile methodologies and contributing to iterative development processes.
Role: Data Science & Machine Learning - Other
Industry Type: Pharmaceutical & Life Sciences
Department: Data Science & Analytics
Employment Type: Full Time, Permanent
Role Category: Data Science & Machine Learning
Education
UG: Any Graduate
PG: Any Postgraduate
