- Lilly is proud to be an EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status.
- Our employee resource groups (ERGs) offer strong support networks for their members and are open to all employees.
- Our current groups include: Africa, Middle East, Central Asia Network, Black Employees at Lilly, Chinese Culture Network, Japanese International Leadership Network (JILN), Lilly India Network, Organization of Latinx at Lilly (OLA), PRIDE (LGBTQ+ Allies), Veterans Leadership Network (VLN), Women’s Initiative for Leading at Lilly (WILL), enAble (for people with disabilities). Learn more about all of our groups.
Lilly Recruitment Drive 2025 | Lilly Hiring Software Engineer
The job you are looking for is no longer available..- Company Name
- Lilly
- Company Website
- http://lilly.com
- Job Role
- AI Developer
- Job Type
-
- Fresher
- Experienced
- Job Location
-
- Bengaluru/Bangalore
- Skills
-
- Python
- C++
- SQL
- MySQL
- NoSQL
- Agile
- MongoDB
- Education
-
- BE/BTech
- Branch
-
- IT
- CS
- Job will expire on
- 18 Nov 2025
About Company
Job Overview
- 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.
Eligibility Criteria
- 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.
- 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).
- 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.
- Image classification, object detection, segmentation
- Libraries like OpenCV, Pillow, and Albumentations
- Pretrained models (e.g., ResNet, YOLO, EfficientNet) and transfer learning
- 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.
- 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
- 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.
- 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
- Experience with Git and platforms like GitHub, GitLab, or Bitbucket.
- Familiarity with Agile/Scrum methodologies and tools like JIRA, Trello, or Asana.
- Writing unit tests and integration tests for ML code.
- Using tools like pytest, unittest, and debuggers to ensure code reliability.
Job Description
- 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.
Job Seeker Advisory & Application Guidelines
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