Python ML Developer- Software Product Engineering

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