AI ML Engineer(Technical Support Representative)

Tata Consultancy Services; Chennai District; On-site; INR 700,000 - 1,100,000; Full time

Job summary

Tata Consultancy Services is seeking a technically skilled TSR to support AI/ML products in Chennai. You will troubleshoot model and pipeline issues, assist with API integrations, and provide data-quality and performance monitoring guidance.

The role requires strong Python/SQL proficiency, ML fundamentals, MLOps awareness, and experience with cloud platforms like GCP and Vertex AI. You will work in a 24x7 support context with rotating shifts.

Qualifications

• Proficiency in Python/SQL and understanding of ML frameworks (PyTorch, TensorFlow)
• Strong knowledge of ML/AI fundamentals
• Experience with MLOps and AI ecosystem tools
• Good understanding of GCP compute, storage, IAM, Vertex AI
• Experience in managing GPU/TPU environments
• Debugging, support, and troubleshooting skills
• Knowledge of working with APIs and integration points
• Ability to communicate technical concepts clearly to users
• Ability to support 24x7 operations with rotating shifts
• Excellent English language skills (verbal and written)

Responsibilities

• Troubleshoot model and pipeline issues within the ML lifecycle
• Debug RESTful API integrations between client apps and AI models
• Assist with environment configuration (Docker, Kubernetes, cloud ML services)
• Data quality checks and monitoring model performance for drift and accuracy
• Provide technical documentation and product education to users
• Serve as feedback bridge to engineering, with bug reporting and feature requests
• Translate complex AI concepts for non-technical users and manage high-pressure interactions

Skills

Python

SQL

ML frameworks

GCP

Vertex AI

Docker/Kubernetes

MLOps

Logging & debugging

APIs

English proficiency

Tools

GCP IAM

Job description

Job Description:

• Proficiency in Python/SQL, understanding of ML frameworks (PyTorch, TensorFlow), and data pre-processing logic.


• Core AI/ML Fundamentals knowledge


• Machine Learning Operations (MLOps)


• Should have good understanding about AI/ML Ecosystem Tools


• Strong understanding of GCP compute, storage, IAM, Vertex AI


• Exposure in managing GPU/TPU environments


• Debugging & Support Skills


• Ability to analyze logs, trace errors, and troubleshoot


• Knowledge of working with different APIs


• Identifying, resolving technical issues, and diagnosing the root cause of technical problems.


• Providing technical assistance to users, both internal and external, through various channels


• Experience working with Conversation Agents


• Communicating technical information clearly and concisely to users and stakeholders


• Support with 24x7 operations (Rotational Shifts)


• English language (verbal and written) Proficiency is must

1. Troubleshooting Model & Pipeline Issues

• The core of the role is diagnosing technical failures within the ML lifecycle.


• API & Integration Support: Debugging RESTful API integrations between the client's application and the AI model.


• Inference Failures: Investigating why a model is failing to provide predictions (e.g., timeout issues, memory overflows, or incorrect input data formatting).


• Environment Configuration: Assisting clients with setup issues related to Docker, Kubernetes, or cloud-specific ML environments (AWS SageMaker, Azure ML, etc.).

2. Data & Performance Monitoring

• AI products are only as good as the data fed into them.


• Data Quality Checks: Helping customers identify if their input data is the cause of poor model performance (e.g., missing values, incorrect data types, or schema mismatches).


• Monitoring Drift: Assisting in identifying "Model Drift"where the AI’s performance degrades over time because real-world data has changed compared to the training data.


• Accuracy Inquiries: Explaining to customers why a model produced a specific result using interpretability tools (like SHAP or LIME) or logs.

3. Product Education & Technical Documentation

• Because AI is complex, the TSR serves as a technical teacher.


• Knowledge Base Authoring: Writing guides on "Best Practices for Prompt Engineering" or "How to Fine-tune Hyperparameters" for the specific platform.


• Customer Onboarding: Guiding new technical users through the initial setup of their ML experiments.


• Translating Documentation: Taking complex engineering release notes and making them understandable for the customer's IT team.

4. The "Feedback Bridge" to Engineering

• The TSR is the first to see patterns in how the product fails in the real world.


• Bug Reporting: Identifying and reproducing software bugs in the ML platform and escalating them to the ML Engineers or DevOps team.


• Feature Requests: Aggregating customer feedback regarding missing ML capabilities (e.g., "Customers are asking for support for PyTorch 2.0").


• Edge Case Discovery: Documenting unique edge cases where the AI model consistently fails, which helps the data science team improve future training sets.


• Responsibility for maintaining SLA (Service Level Agreements) and identifying product bugs for the engineering team.


• Translating complex AI concepts for non-technical users and managing high-pressure customer interactions.