ML Engineer

Essential functions

• Design, develop, and train deep learning models on text data (NLP-focused use cases).

• Work on the full ML lifecycle: data preprocessing, feature engineering, model development, training, evaluation, and handoff for deployment.

• Implement NLP techniques such as tokenization, embeddings, sequence modeling, classification, and entity recognition.

• Perform hyperparameter tuning, experiment tracking, and model performance evaluation.

• Analyze and preprocess large-scale text datasets (data cleaning, transformation, ETL pipelines).

• Collaborate with MLOps teams to ensure models are production-ready (packaging, reproducibility, pipeline integration).

• Work closely with vendor/staffing teams to align with SOWs, record IDs, and approval workflows.

• Quickly understand and contribute to existing codebases and ongoing projects.

Qualifications

• Experience:

  • 2–5 years in Machine Learning / Deep Learning (preferred 3–5 years).

  • Candidates with ~2 years may be considered if they have strong, focused NLP/deep learning experience.

• Technical Skills:

  • Strong proficiency in Python.

  • Hands-on experience with PyTorch for deep learning model development.

  • Practical experience in NLP tasks such as text classification, NER, embeddings, or sequence modeling.

• Modeling & ML Skills:

  • Experience with model training, evaluation, and optimization techniques.

  • Familiarity with hyperparameter tuning and experiment tracking.

• Data Skills:

  • Strong data preprocessing and analysis capabilities for text datasets.

  • Experience with ETL pipelines, feature engineering, and data cleaning.

• MLOps Fundamentals:

  • Basic understanding of MLOps concepts such as model packaging, reproducibility, and CI/CD workflows.

  • Familiarity with tools like experiment tracking systems, model registries, and Docker.

• Collaboration & Execution:

  • Ability to quickly ramp up in an existing project environment.

  • Experience working with cross-functional teams, including vendor/staffing processes.

Would be a plus

• Exposure to modern NLP architectures (e.g., transformers), even if not core to role.

• Experience with large-scale data processing frameworks (e.g., Spark).

• Familiarity with cloud platforms (AWS, GCP, or Azure) for ML workflows.

• Prior experience in fintech, transaction data, or entity resolution domains.

• Understanding of data quality, observability, and monitoring frameworks.

• Exposure to production ML systems and scalable deployment practices.

We offer

• Opportunity to work on bleeding-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, sports
• Corporate social events
• Professional development opportunities
• Well-equipped office

About us

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.