Job Profile :

Machine Learning & Deep Learning

1. Design, train, fine?tune, and evaluate machine learning and deep learning models for real?world enterprise use cases.
2. Develop supervised, unsupervised, and self?supervised learning approaches.
3. Implement and optimize neural network architectures (CNNs, RNNs, Transformers).
4. Perform feature engineering, data preprocessing, and model optimization.
5. Define and tune loss functions, evaluation metrics, and validation strategies.
6. LLM & Advanced AI Models
7. Train, fine?tune, and evaluate large language models (instruction tuning, adapters, LoRA).
8. Build domain?specific language models and embedding models.
9. Implement prompt?based and model?based approaches depending on use case requirements.
10. Ensure model robustness, reliability, and explainability.
11. Data & Experimentation
12. Work with structured and unstructured data (SQL, JSON, text, logs).
13. Design experiments, perform error analysis, and iterate models based on results.
14. Document model assumptions, limitations, and performance characteristics.
15. MLOps & Deployment
16. Package and deploy models into production environments.
17. Collaborate with platform and DevOps teams on CI/CD pipelines for ML models.
18. Monitor model performance, drift, and data quality over time.





Delivery & Support model:

•Support model: Standard Business Hours 5x9 (Mo - Fr , 08:00 – 17:00 (CET/CEST, DST included).

What should you bring along :

Expected skill Sets and experience that the candidates should bring along.

Key Qualifications and Skills:

1. Strong background in machine learning and deep learning theory and practice.
2. Proven experience training own models end?to?end (not only consuming APIs).
3. Solid Python skills for data science and ML development.
4. Experience working in enterprise or industrial environments.
5. Structured, analytical mindset with strong problem?solving skills.

Must have technical skill :

1. Python (advanced)
2. Machine Learning & Deep Learning
3. PyTorch and/or TensorFlow
4. Model training, fine?tuning, and evaluation
5. Feature engineering & data preprocessing
6. LLMs and embeddings
7. Experiment tracking and model evaluation

Good to have technical skills :

1. Experience with Transformers and contemporary LLM architectures
2. MLOps tools (MLflow, model registries, monitoring)
3. Distributed training or GPU acceleration
4. Automotive or enterprise data domains
5. Experience with open?source models (LLaMA, Mistral, Qwen)

📌 Data Scientist (Pune)
🏢 BMW TechWorks
📍 Pune