Machine Learning Engineer

Total Experience- 4-6 years

NP- Immediate-15 days

Location- Bangalore

A mid-level ML/Deep-Learning engineer who can do model development & training on text data (NLP + deep learning with PyTorch), has strong Python/data-engineering skills, understands MLOps concepts, and can ramp quickly into an existing project and vendor process.

Role focus / responsibilities

• Develop & train deep-learning models on text data (not necessarily LLMs; more classical/deep NLP).
• End-to-end model lifecycle: data prep → model dev → training → evaluation → handoff to MLOps/deployment team.

Must-have (non-negotiable)

• Languages / frameworks: Python (primary), PyTorch (deep learning).
• Domain: Practical experience building/training deep-learning models for text/NLP (tokenization, embeddings, sequence models, classification/NER/etc.).
• Modeling: Hands-on model training, hyperparameter tuning, experiment tracking, model evaluation.
• Data skills: Strong data analysis / preprocessing for text datasets (ETL, feature engineering, cleaning).
• MLOps familiarity: Basic MLOps — model packaging, reproducible training pipelines, working knowledge of CI/CD for ML or common tooling (experiment tracking, model registry, Docker, basic infra concepts).
• Collaboration / ramp: Able to read existing SOW/rec ID docs, work with vendor teams, and ramp quickly into a live codebase.

Grid Dynamics (Nasdaq:GDYN) is a digital-native technology services provider that accelerates growth and bolsters competitive advantage for Fortune 1000 companies. Grid Dynamics provides digital transformation consulting and implementation services in omnichannel customer experience, big data analytics, search, artificial intelligence, cloud migration, and application modernization. Grid Dynamics achieves high speed-to-market, quality, and efficiency by using technology accelerators, an agile delivery culture, and its pool of global engineering talent. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the US, UK, Netherlands, Mexico, India, Central and Eastern Europe.

To learn more about Grid Dynamics, please visit www.griddynamics.com. Follow us on Facebook, Twitter, and LinkedIn.