Graduate Engineer Analyst –...

codingcircle; Bengaluru; On-site; INR 800,000 - 1,500,000; Full time

Job summary

NTT DATA Bangalore is seeking a Graduate Engineer Analyst for the AIML domain. You will work with AI leaders on Agentic AI and Generative AI initiatives, researching, designing, and deploying AI solutions that scale across industries.

The role emphasizes Python development, ML model training, deployment via REST APIs, and data engineering. Fresh graduates with 0–1 year in CS/IT are encouraged to apply; Bengaluru on-site work model.

Qualifications

• Must have B.Tech / 4-year degree in CS/IT/AI or related field.
• Strong Python coding skills and data science libraries.
• Solid understanding of ML/DL concepts and model validation.
• Experience with data preprocessing, feature engineering, and analysis.
• Good communication and adaptability in a fast-paced environment.

Responsibilities

• Participate in research, design, and deployment of ML/DL models.
• Train, validate, and fine-tune AI models with data preprocessing steps.
• Implement transfer learning for domain-specific use cases.
• Analyze performance metrics and iterate to improve accuracy and speed.

Skills

Python

ML Fundamentals

Data Handling

Problem Solving

Communication

Education

B.Tech / 4-Year Bachelor’s Degree

Tools

PyTorch/TensorFlow

NumPy/Pandas/Scikit-Learn

Job description

NTT DATA Bangalore Full-time Fresher Not Disclosed Posted 21 hours ago

Company :- NTT DATA
Job Title :- Graduate Engineer Analyst – AIML Domain
Role Category / Sub-Category :- Systems Integration Analyst / Other
Experience Level :- 0–1 year (AI domain / projects / internships)
Education Eligibility :- B.Tech / 4-Year Bachelor’s Degree from Tier-1 Colleges
Eligible Branches :- Computer Science, IT, AI / Data Science, Electronics, or related fields
Work Model :- On-Site / Office-based (Bengaluru)
Location :- Bengaluru, Karnataka, India

Role Overview

We are looking for passionate and highly motivated Graduate Engineers to join our AIML team. In this role, you will work closely with AI leaders and enterprise architects on cutting-edge Agentic AI and Generative AI initiatives spanning multiple global industries. This position provides direct exposure to horizontal AI implementation, enabling you to research, design, fine‑tune, and deploy intelligent AI solutions that drive enterprise innovation at scale.

Key Responsibilities

• Algorithm & Model Lifecycle: Participate in the research, design, implementation, and optimization of Machine Learning and Deep Learning models.
• Model Training & Fine‑Tuning: Train, validate, and fine‑tune AI models, defining critical data preprocessing steps and feature engineering pipelines.
• Transfer Learning: Implement transfer learning strategies and curate new training datasets for specialized industry use cases.
• Performance Analysis: Evaluate model performance metrics, analyze error patterns, and formulate iterative strategies for accuracy and speed enhancements.

Application Engineering & Deployment

• Python Application Development: Develop and maintain high‑performance Python‑based applications for end‑to‑end model training and deployment.
• Production Integration & APIs: Deploy trained AI/ML models into enterprise production environments and build RESTful APIs for system integration.
• Agentic & Generative AI: Engineer multi‑agent workflows, autonomous agents, and LLM‑powered Generative AI capabilities for real‑world enterprise automation.
• Interactive UI Development (Optional): Utilize ReactJS to build intuitive user interfaces and dashboards for AI agent monitoring and user interaction.

Data Engineering & Stakeholder Collaboration

• Data Pipelines: Assist in building and maintaining scalable data ingestion, cleaning, and transformation pipelines.
• Exploratory Data Analysis (EDA): Explore, analyze, and visualize multi‑dimensional datasets to uncover hidden patterns, anomalies, and feature relationships.
• Cross‑Functional Collaboration: Partner with AI Product Managers, domain architects, and business stakeholders to translate functional requirements into scalable AI deliverables.

Qualifications & Skill Requirements

Required Skills (MUST HAVE)

• Python Mastery: Strong core proficiency in Python programming, clean coding practices, and data science libraries (e.g., NumPy, Pandas, Scikit‑Learn, PyTorch/TensorFlow).
• Machine Learning Fundamentals: Solid grasp of core ML/DL concepts, model validation methodologies, and supervised/unsupervised algorithms (Classification, Clustering, Regression).
• Data Handling & Manipulation: Proven capability in data preprocessing, missing‑value imputation, feature scaling, and feature extraction.
• Problem‑Solving Skills: Exceptional analytical ability to deconstruct complex technical problems into structured engineering components.
• Communication & Adaptability: Strong verbal and written communication skills with the capability to thrive in a fast‑paced environment.

Good‑to‑Have & Preferred Skills

• Generative & Agentic AI: Exposure to LLMs, Prompt Engineering, RAG (Retrieval‑Augmented Generation) architectures, and Agentic AI frameworks (e.g., LangChain, AutoGen, CrewAI).
• ReactJS / Front‑End: Hands‑on experience or familiarity with ReactJS for building interactive, AI‑driven user interfaces.
• Data Querying & Visualizations: Experience writing SQL queries and building visualizations using tools like Matplotlib, Seaborn, or BI platforms.
• Cloud & MLOps: General understanding of cloud computing platforms (AWS / Azure / GCP) and model deployment containerization (Docker, Kubernetes).

Extra Value Additions & Career Insights

Salary & Compensation Expectations

For early‑career Graduate Engineer Analysts originating from Tier‑1 engineering colleges in Bengaluru, compensation generally ranges between ₹8,00,000 to ₹15,00,000 INR per annum (total CTC), driven by academic standing, coding proficiency, and past project experience in Generative AI or Machine Learning.

Selection & Recruitment Workflow

The evaluation process for this technical role typically spans 3 to 4 rounds:

1. Profile Screening (Tier‑1 College verification, Python/AI Project validation)
2. Online Technical Assessment (Python Coding, Data Structures, ML Algorithms, Math/Stats)
3. Technical Interview Round 1 (Deep‑dive into ML Algorithms, Python Data Wrangling & System Logic)
4. Technical & Architecture Round 2 (GenAI concepts, RAG/Agentic Workflows, Project Case Studies)
5. Managerial & HR Alignment (Culture fit, learning agility, communication evaluation)

Technical Interview Preparation Tips

• Core Machine Learning: Be ready to explain algorithm mechanics from scratch—such as Gradient Descent, Overfitting vs. Underfitting, Bias‑Variance Trade‑off, and Evaluation Metrics (Precision, Recall, F1‑Score, ROC‑AUC).
• Agentic & Generative AI: Review RAG concepts, vector databases (e.g., Pinecone, ChromaDB), embedding models, fine‑tuning techniques (LoRA/PEFT), and how autonomous AI agents execute multi‑step tools.
• Python Live Coding: Practice manipulation of Pandas DataFrames, array operations in NumPy, and basic data structure implementations without third‑party helpers.

Resume Optimization Tips

• Highlight Tier‑1 Credential: Clearly display your university name (IIT, NIT, IIIT, BITS Pilani, or VIT) and your exact B.Tech specialization at the top of your resume.
• Demonstrate Tangible AI Impact: Include personal or academic projects that showcase actual model metrics (Engineered a RAG‑based search system using LangChain & Python, reducing query retrieval latency by 25%).
• Feature Relevant Tech Stacks: Explicitly list Python, PyTorch, Scikit‑Learn, SQL, LangChain, ReactJS, FastAPI/Flask, and REST APIs in your technical summary block.

Key Words for Resume

Incorporate these high‑density keywords into your resume to ensure maximum visibility with ATS scanners:

Machine Learning & Artificial Intelligence

• Graduate Engineer Analyst
• Systems Integration Analyst
• Machine Learning & Deep Learning
• Generative AI & Agentic AI
• Model Fine‑Tuning & Optimization
• Exploratory Data Analysis (EDA)
• Data Preprocessing & Feature Engineering
• Transfer Learning & Model Validation

Technical Stack & Software Engineering

• Python Programming (Data Science Stack)
• ReactJS (Interactive User Interfaces)
• REST API Development & Integration
• SQL & Database Querying
• RAG (Retrieval‑Augmented Generation) Frameworks
• Vector Databases & Embeddings
• Model Deployment & Production Integration
• Data Pipelines & Ingestion Workflow

Frequently Asked Questions (FAQs)

1. What candidate pool is eligible for this role?

This opening is strictly targeted at 0–1 year experienced graduates or freshers holding a 4‑year B.Tech degree in CS, IT, AI/DS, or ECE from Tier‑1 Indian engineering institutions (IIT, NIT, IIIT, BITS Pilani, VIT).

2. Is ReactJS mandatory for this role?

No. ReactJS is listed as a good‑to‑have / optional skill. Strong proficiency in Python and fundamental AI/ML concepts are the primary mandatory technical requirements.

3. Where is this position located?

The position is based in Bengaluru, Karnataka, India.

4. What sets this role apart from a traditional Data Analyst role?

Unlike traditional reporting analytics, this role focuses on Systems Integration for AI—building functional Python applications, developing data engineering pipelines, training/fine‑tuning ML models, and deploying Agentic and Generative AI frameworks directly into enterprise production systems.