Genomics AI Engineer Intern-Advanced Intelligence and Research at Lilly

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

12 Jul, 26

Salary

0.0

Posted On

13 Apr, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Machine Learning, Deep Learning, Cheminformatics, RDKit, PyTorch, TensorFlow, Generative AI, LLMs, MLOps, Drug Discovery, ADMET Prediction, Data Integration, SQL, Cloud Computing, Git

Industry

Pharmaceutical Manufacturing

Description

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 employees around the world 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 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. Competency Summary We are seeking an AI Engineer Intern to design, build, and deploy AI/ML models and intelligent systems that accelerate small molecule drug discovery — from hit identification and lead optimization through to candidate selection. You will work alongside medicinal chemists, computational chemists, and DMPK scientists to develop and productionize deep learning, generative AI, and LLM-powered solutions that extract insights from compound activity, structure, and ADMET data. This role sits at the heart of our computational chemistry ecosystem, applying AI to connect experimental data, predictive models, and public chemical databases into intelligent workflows that enable faster, smarter molecule design. Key Objectives/Deliverables Design and deploy ML pipelines that ingest compound registration, assay activity, and structure-activity relationship (SAR) data to train and serve predictive models for molecular property prediction and hit prioritization Build AI-powered data integration layers that connect public chemical databases (ChEMBL, PubChem, BindingDB, DrugBank) with internal compound management systems to enrich training datasets for ML models. Develop and fine-tune deep learning models on molecular representations (SMILES, molecular graphs, 3D conformers, fingerprints) for tasks such as ADMET prediction, binding affinity estimation, and de novo molecule generation Build and deploy generative AI and LLM-based applications for molecular design, retrosynthesis planning, and automated SAR analysis, including retrieval-augmented generation (RAG) over chemistry literature and patents Implement model evaluation frameworks, monitoring, and MLOps practices (experiment tracking, model versioning, CI/CD) to ensure reliability and reproducibility of AI systems in production. Partner closely with medicinal chemists, computational chemists, and data scientists to translate discovery questions into AI-driven solutions and well-defined model requirements. Minimum Position Qualifications Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Computational Chemistry, Cheminformatics, or a related quantitative field. Ph.D. preferred. Understanding of small molecule drug discovery workflows — HTS, lead optimization, SAR, selectivity profiling, and ADMET characterization — and how AI/ML can be applied at each stage Working understanding of chemical representations (SMILES, InChI, MOL/SDF), molecular descriptors, and molecular graph structures, with the ability to featurize them for ML model training. Proficiency in Python, including ML frameworks (PyTorch, TensorFlow) and cheminformatics libraries (RDKit, NumPy, pandas, scikit-learn). Experience with Hugging Face or molecular generative models is a plus. Experience with Git/GitHub workflows, CI/CD fundamentals, and exposure to MLOps tooling (MLflow, Weights & Biases, Docker) for experiment tracking and model deployment. Strong proficiency in Python for data processing and model development; familiarity with SQL and cloud-based AI platforms (AWS SageMaker, Databricks, or Azure ML). Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form (https://careers.lilly.com/us/en/workplace-accommodation) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response. Lilly does not discriminate on the basis of age, race, color, religion, gender, sexual orientation, gender identity, gender expression, national origin, protected veteran status, disability or any other legally protected status. #WeAreLilly At Lilly we strive to ensure our employees are part of a team that cares about them and our shared purpose of making life better for those around the world. How do we do this? We continue to look for ways to include, innovate, accelerate and deliver while maintaining integrity, excellence and respect for people. We hope that you seek to join us on our journey as we create medicine and deliver improved outcomes for patients across the globe! #WeAreLilly

Responsibilities

The intern will design and deploy AI/ML models to accelerate small molecule drug discovery and lead optimization. They will also build data integration layers and MLOps pipelines to support predictive modeling and generative AI applications.