Data Scientist

Role description

Design develop and deploy MLAI models for real time and batch use cases including model experimentation training and evaluation.

Build and optimize inference pipelines and integrate ML capabilities into applications services in partnership with product and engineering teams.

Develop and maintain data pipelines for model training validation and continuous improvement retraining continual learning.

Monitor model performance in production including quality drift bias hallucinations where applicable and drive improvements in reliability and robustness.

Establish engineering best practices for ML delivery reproducibility versioning testing documentation and benchmarking experimentation.

Contribute to solution architecture decisions for ML systems data compute deployment patterns and operational controls.

Mentor junior engineers and lead technical reviews for ML code, pipelines and deployment implementations. 7-12 years of experience in software engineering data engineering ML engineering with significant hands on time delivering ML solutions.

Strong proficiency in Python and MLDL libraries such as PyTorch, Tensor Flow and familiarity with modern model ecosystems e.g. Hugging Face.

Solid understanding of ML fundamentals including feature engineering, model selection, evaluation metrics, overfitting, cross validation and deep learning concepts (neural nets, transformers where relevant).

Experience with model deployment approaches and tools e.g. model serving ONNX, Torch Serve, Triton or equivalent.

Strong engineering practices including clean code, debugging, performance optimization, API integration and collaboration in cross functional teams.

Experience with MLOps, GenAI Ops tooling such as MLflow, containerization (Docker) and cloud platforms (AWS, Azure, GCP) for scalable ML delivery. Experience with LLMs, Generative AI, fine tuning, prompt engineering, evaluation and production patterns.

Familiarity with RAG and vector databases plus responsible ethical AI practices and governance.

Experience building automated benchmarking, AB testing and monitoring frameworks for ML systems.

Contributions to open source, publications, patents or strong internal innovation track record. Strong ownership and ability to lead quality outcomes end-to-end.

Clear communication and stakeholder management.

Mentoring mindset and collaboration across QA, Dev and Dev Ops teams.

Actual compensation within the range will be dependent upon the individual's skills, experience, performance and internal equity.

• Comprehensive Medical Plan Covering Medical, Dental, Vision
• Short Term and Long-Term Disability Coverage
• 401(k) Plan with Company match
• Life Insurance
• Paid Paternity and Maternity Leave
• Vacation Time, Sick Leave, Paid Holidays

The range displayed on each job posting reflects the minimum and maximum salary target for the position across all US locations. Within the range, individual pay is determined by work location and job level and additional factors including job-related skills, experience, and relevant education or training. Depending on the position offered, other forms of compensation may be provided as part of overall compensation like an annual performance-based bonus, sales incentive pay and other forms of bonus or variable compensation.

Role description is repeated for emphasis in the original; the refined version consolidates content above.

Actual compensation within the range will be dependent upon the individual's skills, experience, performance and internal equity.

Disclaimer
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The compensation and benefits information provided herein is accurate as of the date of this posting. LTIMindtree is an equal opportunity employer that is committed to diversity in the workplace. Our employment decisions are made without regard to race, color, creed, religion, sex (including pregnancy, childbirth or related medical conditions), gender identity or expression, national origin, ancestry, age, family-care status, veteran status, marital status, civil union status, domestic partnership status, military service, disability or history of disability, genetic information, atypical hereditary cellular or blood trait, union affiliation, affectional or sexual orientation or preference, or any other characteristic protected by applicable federal, state, or local law.