AI/ML Model Validation Specialist

Tata Consultancy Services; Mumbai; On-site; INR 800,000 - 1,200,000; Full time

Job summary

A leading technology consulting firm is seeking an AI/ML Model Validation Specialist to ensure rigorous validation of AI models throughout their lifecycle. This role involves evaluating model design, assessing data integrity, and generating validation reports while working closely with developers and business leaders. Ideal candidates will hold a graduate degree in a quantitative field and have over 3 years of relevant experience in the AI domain, alongside strong programming and communication skills.

Qualifications

• Graduate degree (Masters or PhD preferred) in a quantitative field.
• 3+ years of experience in model development or validation.
• Experience validating LLMs, GenAI, NLP, or computer vision models.

Responsibilities

• Conduct independent technical validation of AI/ML models.
• Evaluate methodology and technical design of AI models.
• Perform benchmarking and sensitivity analysis on models.
• Assess data sources for biases.
• Identify risks associated with model outputs.
• Author validation reports for transparency.
• Collaborate with stakeholders on findings.
• Keep up with emerging AI technologies.

Skills

Proficiency in Python

Proficiency in R

Understanding of ML frameworks

Excellent communication skills

Education

Graduate degree in a quantitative field

Tools

TensorFlow

PyTorch

scikit-learn

AWS

GCP

Azure

Job description

We are seeking a highly skilled and analytical AI/ML Model Validation Specialist to join our Risk Management/Data Science team. The successful candidate will perform independent reviews of artificial intelligence and machine learning models, including Generative AI (GenAI), predictive analytics, and natural language processing (NLP) models. You will be responsible for challenging model design, data integrity, and performance to ensure compliance with internal policies and regulatory guidelines (e.g., SR 11-7, EU AI Act).

Key Responsibilities

• Independent Validation: Conduct rigorous, independent technical validation of AI/ML models throughout their life cycle (pre-development, post-development, and post-implementation).
• Methodology Review: Evaluate the conceptual soundness, mathematical logic, and technical design of AI models.
• Performance Testing: Perform benchmarking, sensitivity analysis, and robustness testing to ensure models behave as expected under various scenarios.
• Data Validation: Assess the appropriateness of data sources, training sets, and data processing techniques to detect biases.
• Risk Assessment: Identify potential weaknesses, biases, and risks (operational, ethical, or financial) associated with model outputs.
• Documentation & Reporting: Author comprehensive validation reports outlining findings, limitations, and recommendations for model enhancement, ensuring transparency for regulators.
• Stakeholder Engagement: Collaborate with developers, data scientists, and business leaders to discuss findings and track remediation efforts.
• Stay Current: Keep up-to-date with emerging AI technologies, validation techniques, and regulatory developments.

Required Qualifications

• Education: Graduate degree (Masters or PhD preferred) in a quantitative field such as Statistics, Mathematics, Computer Science, Data Science, Physics, or Engineering.
• Experience: 3+ years of hands‑on experience in model development, validation, or risk management, specifically within the AI/ML domain.
• Technical Skills:
  • Proficiency in Python, R, or similar programming languages.
  • Strong understanding of ML frameworks (TensorFlow, PyTorch, scikit‑learn).
  • Familiarity with cloud platforms (AWS, GCP, or Azure).
• AI Specialization: Experience validating LLMs, GenAI, NLP, or computer vision models is highly desirable.
• Communication: Excellent written and verbal communication skills, with the ability to explain complex technical concepts to non‑technical stakeholders.

Preferred Qualifications

• Knowledge of Model Risk Management (MRM) frameworks and regulatory guidelines (e.g., SR 11-7, OCC, Basel, EU AI Act).
• Experience in the Banking or Financial Industry.
• Familiarity with Explainable AI (XAI) tools (SHAP, LIME, Fairlearn).