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Technology Analyst (Generative AI & Agentic AI)

Company
Company Infosys
Type
Opportunity Type Full-Time Job
Salary
Stipend / Salary 12 - 30 LPA
Location
Location Bangalore, Karnataka, India
Posted Date
Posted Date Today
Python Generative AI Large Language Models Retrieval‑Augmented Generation Prompt Engineering Agentic AI Vector Databases REST API Development Cloud Platforms (AWS/Azure/GCP) Docker Kubernetes CI/CD Agile Methodologies
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Comprehensive Placement Papers Collection Open Resource ↗
Curated set of Infosys placement papers to practice aptitude, reasoning, and technical questions.
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Interview Experience and Process Guide Open Resource ↗
First‑hand accounts of Infosys recruitment stages, useful for preparing coding and technical rounds.
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Preparation Hub for Infosys Interviews Open Resource ↗
Aggregated resources covering coding practice, interview tips, and company‑specific insights.
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Algorithm Practice Platform Open Resource ↗
Extensive problem set to sharpen data‑structures and algorithms skills required for the online test.

Educational Qualification: MCA, MSc, MTech, B.E., BCA, BSc or BTech (any branch). Minimum aggregate: not explicitly specified, but candidates should have a solid academic record. Experience: 3–5 years of professional experience in software development, preferably with Python and AI/ML projects. Technical Requirements: Proficiency in Python, experience with Generative AI, LLMs, Retrieval‑Augmented Generation, prompt engineering, vector databases, and enterprise API integrations. No specific backlog policy mentioned; candidates should have a clean academic record as per Infosys norms. Batch year: any recent graduate who meets the experience criteria.

1
Round 1: Online Coding Assessment (Python/DSA)
2
Round 2: Technical Interview (AI concepts, system design, coding)
3
Round 3: HR Interview (fit, communication, expectations)
Infosys Limited is a global leader in consulting, technology, and next‑generation services. With a presence in over 50 countries, Infosys helps enterprises transform their business models through digital, cloud, and AI‑driven solutions. The Digital Experience service line focuses on creating end‑to‑end digital products, platforms, and experiences that enable clients to stay ahead in a hyper‑connected world. As part of its AI‑first strategy, Infosys is building next‑generation Generative AI and Agentic AI solutions that can automate knowledge work, enhance decision‑making, and deliver personalized experiences at scale. The role of Technology Analyst (Generative AI & Agentic AI) is positioned within the Digital Experience team and is aimed at professionals with 3‑5 years of hands‑on experience in Python development and AI engineering. The analyst will design, develop, and maintain AI‑powered applications that leverage large language models (LLMs), Retrieval‑Augmented Generation (RAG) pipelines, and autonomous agentic workflows. Collaboration with business stakeholders, data scientists, and solution architects is essential to translate complex requirements into production‑grade AI services. **Key Responsibilities** 1. Design and implement Generative AI applications using LLM APIs and Python backend services. 2. Build and optimise RAG pipelines, including data ingestion, chunking, embedding generation, vector store management, and context‑aware generation. 3. Develop Agentic AI systems capable of multi‑step reasoning, tool calling, and workflow orchestration. 4. Engineer robust prompt‑engineering strategies, conduct A/B testing, and iteratively improve response quality. 5. Create and maintain AI evaluation frameworks to measure accuracy, relevance, hallucination rates, and business KPIs. 6. Integrate AI services with enterprise applications, REST APIs, databases, and cloud platforms (AWS/Azure/GCP). 7. Develop scalable backend micro‑services in Python, ensuring high availability and observability. 8. Monitor production AI systems, troubleshoot performance issues, and implement continuous improvement loops. 9. Collaborate with cross‑functional teams to gather requirements, define technical specifications, and deliver end‑to‑end solutions. 10. Stay updated with emerging AI research, tools, and best practices, and mentor junior engineers. **Tech Stack**: Python, FastAPI/Flask, LangChain, OpenAI/Anthropic APIs, Vector DBs (Pinecone, Milvus, Weaviate), Docker, Kubernetes, CI/CD pipelines, Git, AWS/Azure services. **Growth Path**: Successful analysts can progress to Senior Analyst, AI Solution Architect, or AI Product Manager roles, with opportunities to lead large‑scale AI programmes across industries. **Why Join Infosys**: Infosys offers a structured learning ecosystem, exposure to Fortune‑500 clients, and a culture that rewards innovation. Employees benefit from global mobility, continuous up‑skilling programs, and a supportive work‑life balance that encourages both personal and professional growth.

Infosys — AI & Machine Learning Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

APTITUDE & LOGIC 📖
Aptitude Practice Questions & Online Mock Tests

Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.

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COMPANY GUIDE 🎯
Infosys Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Infosys and off-campus tech roles.

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MOCK TESTS & PAPERS 📝
Comprehensive Interview Prep Resources & Syllabus

Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.

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CODING PRACTICE 💻
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

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Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (Python, Generative AI, Large Language Models, Retrieval‑Augmented Generation, Prompt Engineering, Agentic AI, Vector Databases, REST API Development, Cloud Platforms (AWS/Azure/GCP), Docker, Kubernetes, CI/CD, Agile Methodologies) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
Infosys Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
What is the bias-variance tradeoff and how do you prevent overfitting? Answer ▼
Model Answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer ▼
Model Answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer ▼
Model Answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer ▼
Model Answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Why do you want to join Infosys as a Technology Analyst (Generative AI & Agentic AI)?
Preparation Tip: Highlight Infosys's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

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