Specialist - Data Sciences

LTM; Irving (TX); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Medical coverage

Dental coverage

Vision coverage

Disability insurance

401(k)

Life Insurance

Paid time off

Parental Leave

Job summary

LTIMindtree in Irving, TX is hiring a Gen AI Engineer to design, develop and deploy MLAI models for real time and batch use cases. You will build and optimize inference pipelines and integrate ML capabilities with product and engineering teams.

You will develop data pipelines for model training, validation and continual improvement, monitor production model performance, and drive reliability and robustness across systems.

Qualifications

• Experience designing and deploying ML/AI models in production.
• Strong Python development skills and ML/DL libraries.
• Experience with model deployment tools (ONNX, Torch Serve, Triton).
• Hands-on experience with MLOps and cloud platforms.

Responsibilities

• Design, develop and deploy MLAI models for real time and batch use cases.
• 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 production model performance 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.
• Clear communication and stakeholder management.

Skills

Python

ML/DL libraries

PyTorch

TensorFlow

Hugging Face

MLOps

Docker

Cloud platforms

Generative AI

Tools

ONNX

TorchServe

Triton

MLflow

Docker

Job 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 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 712 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 TensorFlow and familiarity with modern model ecosystems eg Hugging Face
• Solid understanding of ML fundamentals feature engineering model selection evaluation metrics overfitting cross validation and deep learning concepts neural nets transformers where relevant
• Experience with model deployment approaches tools eg model serving ONNX Torch Serve Triton or equivalent
• Strong engineering practices clean code debugging performance optimization API integration and collaboration in cross functional teams
• Experience with MLOps GenAIOps 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

Role Description

Title: Gen AI Engineer

Location Irving, Tx (Hybrid)

• 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 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 712 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 TensorFlow and familiarity with modern model ecosystems eg Hugging Face
• Solid understanding of ML fundamentals feature engineering model selection evaluation metrics overfitting cross validation and deep learning concepts neural nets transformers where relevant
• Experience with model deployment approaches tools eg model serving ONNX Torch Serve Triton or equivalent
• Strong engineering practices clean code debugging performance optimization API integration and collaboration in cross functional teams
• Experience with MLOps GenAIOps 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

Other Details

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

Benefits/perks listed below may vary depending on the nature of your employment with LTIMindtree (“LTIM”):

Benefits And Perks

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

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.

Disclaimer: 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, handicap or disability or history of handicap or 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, except where such considerations are bona fide occupational qualifications permitted by law.