ML Engineer (GenAI /Data Scientist)

Tata Consultancy Services; Bengaluru, Kolkata District; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

Tata Consultancy Services in Bengaluru seeks a senior ML/Data Scientist to drive GenAI and agent-based solutions. The role focuses on understanding business context with stakeholders, developing MVPs to production, and ensuring responsible AI practices.

You will balance speed, accuracy, and governance while enabling scalable, autonomous workflows and system explanations. The candidate should have strong programming skills, experience with LLM APIs, and proficiency in Python/R, TensorFlow, and

Qualifications

• Bachelor's degree in Science or Engineering or equivalent.
• 5+ years of work experience as a Data Scientist / ML Engineer.
• Strong programming and analytical skills with GenAI experimentation and agent behavior tuning.
• Experience with LLM APIs and AI services.

Responsibilities

• Collaborate with business stakeholders to identify opportunities for GenAI and agent-based systems.
• Develop MVPs/POCs of GenAI-powered solutions and guide production deployment.
• Balance speed, analytics, model autonomy, and human-in-the-loop controls.
• Explore agent orchestration, prompts, tools, and memory mechanisms for robustness.
• Build analytical/m modeling solutions using Python, R, TensorFlow, LLMs, vector databases.
• Create model-based solutions combining ML with simulations, optimization, and autonomous workflows.
• Design and visualize solutions for business users and communicate AI system behavior explanations.
• Develop GenAI pipelines to extract insights from large, unstructured data sets.
• Deploy GenAI/Agentic AI systems to production and monitor drift and risk.
• Compare ML methods and recommend techniques based on accuracy and cost.
• Establish automated processes for model and prompt evaluation and validation.
• Advance AI capabilities across data science, cognitive engineering, conversational AI, and multi-agent systems.
• Ensure responsible AI with bias mitigation and auditability.

Skills

GenAI concepts

Agentic AI

Prompt engineering

ML engineering

Data science

Stakeholder communication

Agile delivery

Education

Bachelor's degree in Science or Engineering

Tools

Python

R

TensorFlow

Azure ML

Databricks

SQL

Job description

Major Duties & Responsibilities -

Work with business stakeholders and cross-functional SMEs to deeply understand business context, key business questions, and opportunities where GenAI and agent-based systems can augment decision-making and automation.

Create Proof of Concepts (POCs) / Minimum Viable Products (MVPs) including GenAI-powered solutions (e.g., LLM-based assistants, copilots, RAG systems), then guide them through to production deployment and operationalization.

Make solution recommendations that appropriately balance speed to market, analytical soundness, model autonomy, and human-in-the-loop controls.

Explore design options to assess efficiency and impact, including agent orchestration strategies, prompt strategies, tool-use patterns, and memory mechanisms to improve robustness and rigor.

Develop analytical / modeling solutions using a variety of commercial and open-source tools (e.g., Python, R, TensorFlow, LLMs, prompt frameworks, vector databases).

Formulate model-based solutions by combining machine learning algorithms with other techniques such as simulations, optimization, and agentic workflows that plan, reason, and act autonomously within defined guardrails.

Design, adapt, and visualize solutions based on evolving requirements and communicate them through presentations, scenarios, stories, and AI system behavior explanations for business users.

Create algorithms and GenAI pipelines to extract, summarize, reason over, and generate insights from large, multi-parametric and unstructured data sets (text, documents, images).

Deploy ML and GenAI/Agentic AI systems to production to identify actionable insights from large databases, APIs, and enterprise systems.

Compare results from various methodologies including classical ML, deep learning, and GenAI approaches, and recommend optimal techniques based on accuracy, cost, latency, and risk.

Develop and embed automated processes for model and prompt evaluation, predictive model validation, deployment, monitoring, and drift detection (data, model, and prompt drift).

Work on multiple pillars of AI including data science, cognitive engineering, conversational AI, enterprise copilots, and multi-agent systems.

Ensure that solutions exhibit high levels of performance, security, scalability, maintainability, repeatability, reusability, reliability, and responsible AI compliance (bias, hallucination control, auditability).

Provide thought leadership and subject matter expertise in machine learning, GenAI, and Agentic AI, making impactful contributions to internal discussions on emerging practices, architectures, and governance.

Required Qualifications -

Bachelor of Science or Bachelor of Engineering at a minimum.

5+ years of work experience as a Data Scientist / ML Engineer.

Strong combination of business focus, analytical thinking, and programming skills to rapidly cycle hypotheses, including GenAI experimentation and agent behavior tuning.

Advanced skills with statistical/programming software (e.g., Python, R) and data querying languages (e.g., SQL, Hadoop/Hive, Scala), plus experience integrating LLM APIs and AI services.

Strong hands-on skills in feature engineering, hyperparameter optimization, and prompt engineering / prompt evaluation techniques.

Experience producing high-quality, production-ready code, tests, and documentation, including AI system design artifacts.

Experience with Microsoft Azure or AWS data and AI platforms (e.g., Azure ML, Azure OpenAI, Databricks).

Strong understanding of descriptive and exploratory statistics, predictive modeling, ML algorithms, optimization, forecasting, deep learning, and GenAI system evaluation metrics.

Proficiency in statistical concepts, ML algorithms, and foundational GenAI concepts (transformers, embeddings, RAG, function/tool calling).

Good knowledge of Agile principles and iterative AI product delivery.

Ability to lead, manage, build, and deliver customer business results through data scientists or professional services teams, including GenAI solution delivery.

Ability to clearly communicate assumptions, AI limitations, risks, and results to both technical and non-technical stakeholders.

Self-motivated, proactive problem solver who can work independently and collaboratively in fast-evolving AI environments.