Artificial Intelligence Engineer

Organization Overview
:

At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our 39,000 employees work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We're looking for people who are determined to make life better for people around the globe.

Role Summary:

We are looking for a skilled and innovative
AI Developer
to design, develop, and deploy AI-powered applications and services. The ideal candidate will have hands-on experience with machine learning, deep learning, and natural language processing and will be responsible for building intelligent systems that solve real-world problems. This role requires strong programming skills, a solid understanding of AI/ML algorithms, and the ability to work collaboratively in a fast-paced environment.

Responsibilities:

• Develop and implement machine learning models and algorithms for classification, regression, clustering, recommendation, and more.
• Build and maintain data pipelines for training and inference workflows.
• Collaborate with data scientists, product managers, and software engineers to integrate AI models into production systems.
• Optimize model performance and scalability for real-time and batch processing.
• Conduct experiments, evaluate model performance, and iterate based on results.
• Stay up-to-date with the latest research and advancements in AI/ML and apply them to practical use cases.
• Document code, processes, and model behavior for reproducibility and compliance.

Basic Requirements:

1. Programming Languages

• Python: Core language for AI/ML development. Proficiency in libraries like
• NumPy, Pandas for data manipulation
• Matplotlib, Seaborn, Plotly for data visualization
• Scikit-learn for classical ML algorithms
• Familiarity with R, Java, or C++ is a plus, especially for performance-critical applications.

2. Machine Learning & Deep Learning Frameworks

Experience building models using the following:

• TensorFlow and Keras for deep learning
• PyTorch for research-grade and production-ready models
• XGBoost, LightGBM, or CatBoost for gradient boosting
• Understanding of model training, validation, hyperparameter tuning, and evaluation metrics (e.g., ROC-AUC, F1-score, precision/recall).

3. Natural Language Processing (NLP)

Familiarity with:

• Text preprocessing (tokenization, stemming, lemmatization)
• Vectorization techniques (TF-IDF, Word2Vec, GloVe)
• Transformer-based models (BERT, GPT, T5) using Hugging Face Transformers
• Experience with text classification, named entity recognition (NER), question answering, or chatbot development.

4. Computer Vision (CV)

Experience with:

• Image classification, object detection, segmentation
• Libraries like OpenCV, Pillow, and Albumentations
• Pretrained models (e.g., ResNet, YOLO, EfficientNet) and transfer learning

5. Data Engineering & Pipelines

• Ability to build and manage data ingestion and preprocessing pipelines.
• Tools: Apache Airflow, Luigi, Pandas, Dask
• Experience with structured (CSV, SQL) and unstructured (text, images, audio) data.

6. Model Deployment & MLOps

Experience deploying models as:

• REST APIs using Flask, FastAPI, or Django
• Batch jobs or real-time inference services
• Familiarity with:
• Docker for containerization
• Kubernetes for orchestration
• MLflow, Kubeflow, or SageMaker for model tracking and lifecycle management

7. Cloud Platforms

• Hands-on experience with at least one cloud provider:
• AWS (S3, EC2, SageMaker, Lambda)
• Google Cloud (Vertex AI, BigQuery, Cloud Functions)
• Azure (Machine Learning Studio, Blob Storage)
• Understanding of cloud storage, compute services, and cost optimization.

8. Databases & Data Access

Proficiency in:

• SQL for querying relational databases (e.g., PostgreSQL, MySQL)
• NoSQL databases (e.g., MongoDB, Cassandra)
• Big data tools like Apache Spark, Hadoop, or Databricks is a plus

9. Version Control & Collaboration

• Experience with Git and platforms like GitHub, GitLab, or Bitbucket.
• Familiarity with Agile/Scrum methodologies and tools like JIRA, Trello, or Asana.

10. Testing & Debugging

• Writing unit tests and integration tests for ML code.
• Using tools like pytest, unittest, and debuggers to ensure code reliability
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