AI / ML Engineer

Role Overview

We seek an AI / ML Engineer experienced in software engineering fundamentals and adept at creating scalable, production-grade Python systems, data pipelines, and machine learning solutions deployed in cloud environments. The candidate should have hands-on experience with the entire machine learning lifecycle including data preprocessing, feature engineering, model creation, evaluation, deployment, monitoring, and documentation.

Key Responsibilities

• Design, develop, and maintain AI-driven models and systems leveraging Python and modern machine learning libraries.
• Create and optimize data pipelines handling diverse data formats such as CSV, JSON, XML, and relational databases.
• Conduct data preprocessing, feature engineering, exploratory data analysis, model training, validation, and performance assessments.
• Implement ML solutions for tasks like classification, forecasting, embeddings, NLP, computer vision, similarity search, and intelligent automation.
• Build scalable ETL/ELT workflows with Python, Snowflake, and cloud services including AWS S3, Lambda, and Glue.
• Support deployment-ready workflows with model monitoring, data quality validation, logging, and error management.
• Collaborate cross-functionally with data scientists, software engineers, product teams, and stakeholders to deliver applicable AI/ML solutions.
• Conduct experimental comparisons of model architectures, tune hyperparameters, analyze performance metrics, and document findings.
• Write reusable, maintainable, and thoroughly tested code following software engineering best practices including Git workflows and CI/CD standards.
• Stay up-to-date with advancements in AI, machine learning, deep learning, NLP, computer vision, embeddings, and cloud AI/ML platforms.

Required Skills and Experience

• Minimum four years of professional experience in software engineering, data engineering, machine learning engineering, or related fields.
• Proficiency in Python programming with knowledge of SQL; familiarity with R or C is advantageous.
• Experience using scientific Python and machine learning libraries such as NumPy, Pandas, Matplotlib, scikit-learn, SciPy, PyTorch, TensorFlow, HuggingFace, and Transformer models.
• Developed ML models including Random Forest, ensemble methods, deep learning, NLP, computer vision, and embedding retrieval systems.
• Deep understanding of data preprocessing, feature engineering, model evaluation metrics, handling class imbalance, validation approaches, and statistical testing.
• Skilled in designing and maintaining scalable ETL/ELT pipelines using Snowflake, AWS, and Python.
• Working knowledge of cloud services, primarily AWS (S3, Lambda, Glue), and cloud-native data infrastructure.
• Experience with data quality monitoring, schema management, anomaly detection, logging, and ensuring pipeline reliability.
• Familiarity with Docker, CI/CD pipelines, Git-based collaboration, technical documentation, and production software development standards.
• Ability to effectively communicate complex technical concepts to both technical and nontechnical audiences.

Preferred Qualifications

• Knowledge of MLOps practices, including model versioning, experiment tracking, deployment, monitoring, and automated retraining.
• Experience with multimodal AI applications involving image-text embeddings, semantic search, content-based retrieval, and vector similarity search.
• Hands-on experience in computer vision use cases such as CNN-based classification, image feature extraction, and dataset quality assessment.
• Expertise with NLP tasks including BERT/Transformer fine-tuning, speech or text classification, and emotion or intent detection.
• Familiarity with large-scale datasets, data warehousing, star schema design, outlier detection, and analytics-ready data modeling.
• Research or publication experience, or demonstrated ability to convert research into applied machine learning solutions.
• AWS certification or equivalent cloud credentials.
• Experience developing lightweight web applications or APIs for ML model deployment, e.g., using Flask.

Education

Bachelor’s or Master’s degree in Computer Science, Data Analytics, Information Technology, Artificial Intelligence, Machine Learning, Statistics, or a related discipline.

Additional Details

Compensation depends on individual skills, experience, performance, and internal equity. Benefits vary depending on employment nature and include comprehensive medical, dental, vision coverage, short- and long-term disability, 401(k) with company match, life insurance, paid vacation, sick leave, holidays, and paid parental leave.

The listed salary range reflects the minimum and maximum target across all US locations with individual pay determined by work location, job level, skills, experience, and education. Additional compensation may include performance bonuses and variable incentives.

LTIMindtree is an equal opportunity employer committed to workplace diversity and makes employment decisions without regard to legally protected characteristics.