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REQUIREMENT_ID_2586 • 3-DAY_ACTIVE_POLICY N

Lead AI Full Stack Engineer

Company
Company Persistent
Type
Opportunity Type Full-Time Job
Salary
Stipend / Salary Competitive Package (Best in Industry)
Location
Location Pune, India
Posted Date
Posted Date Today
Apply on Official Website ↗ 🤝 Connect Recruiter ↗
PE
Persistent Technical Recruiter
Talent Acquisition Partner
🏢 Persistent • Direct Recruiter Email: recruiter@persistent.com
Angular Full Stack Artificial Intelligence Java React.js Stack
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Aptitude Practice Questions & Mock Tests Open Resource ↗
Curated logical, quantitative, and verbal reasoning problems for the initial online screening round.
🎯
Company-Specific Interview Preparation Corner Open Resource ↗
Detailed interview experiences, exam formats, and previous test questions for Persistent and tech roles.
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Technical Placement Cheat Sheets & Question Bank Open Resource ↗
High-yield coding cheat sheets, core CS fundamentals (OOP, DBMS, OS, Networks), and rapid revision guides.
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Algorithm, DSA & Live Code Debugger Practice Open Resource ↗
Hands-on problem sets to improve coding speed and step-by-step memory debugging.

Experience: 12-15 Yrs. Open to matching qualifications.

1
Online Assessment / Technical Interview Rounds / HR Review
About Position: We are seeking an experienced Lead AI Full Stack Engineer Agentic AI to design, develop, and deliver enterprise-grade AI-powered applications and intelligent agent ecosystems. The ideal candidate will possess deep expertise in full-stack software development, Generative AI, Large Language Models (LLMs), Agentic AI frameworks, cloud-native architectures, and enterprise application delivery. This role requires a combination of technical leadership, hands-on engineering, and architectural expertise to build scalable AI solutions that drive business innovation and operational excellence. Role: Lead AI Full Stack Engineer Agentic AI Location: Pune Experience: Between 12 to 15 Years Job Type: Full Time Employment What You'll Do: Design, develop, and deploy enterprise-grade Agentic AI applications and intelligent automation solutions. Build scalable backend services using Java (Spring Boot) and/or Python (FastAPI, Django). Develop modern and responsive user interfaces using React, Angular, and TypeScript. Design and implement REST APIs, microservices, and event-driven architectures. Integrate Large Language Models (LLMs) into enterprise applications and workflows. Build Retrieval-Augmented Generation (RAG) solutions using vector databases and embeddings. Design agentic workflows, multi-agent systems, orchestration frameworks, and intelligent decision-making pipelines. Collaborate with product owners and business stakeholders to translate requirements into scalable AI solutions. Implement automated testing across unit, integration, API, and end-to-end testing layers. Drive cloud-native application development using AWS and containerized platforms. Develop and maintain CI/CD pipelines, observability solutions, and deployment automation frameworks. Ensure solution scalability, security, reliability, and compliance with enterprise standards. Mentor engineering teams and establish best practices for AI engineering and software development. Lead architecture reviews, technical design discussions, and technology evaluations. Drive continuous innovation by exploring emerging Generative AI, LLM, and Agentic AI capabilities. Expertise You'll Bring: 12 to 15 years of experience in software engineering, full-stack development, and enterprise application delivery. Strong expertise in Java (Spring Boot) and/or Python (FastAPI, Django). Extensive experience building enterprise web applications using React or Angular and TypeScript. Strong understanding of API design, microservices architecture, and distributed systems. Experience working with relational and NoSQL databases. Hands-on experience implementing automated testing strategies across multiple testing layers. Strong expertise integrating Large Language Models (LLMs) into enterprise applications. Experience with prompt engineering, prompt optimization, and AI workflow design. Hands-on knowledge of Retrieval-Augmented Generation (RAG), vector databases, embeddings, and semantic search. Experience implementing LLM evaluation frameworks, guardrails, and responsible AI practices. Strong understanding of AI agent architectures, agent orchestration, and autonomous workflows. Expertise in AWS cloud services and cloud-native solution development. Hands-on experience with Docker, Kubernetes, and container orchestration platforms. Strong understanding of CI/CD pipelines, DevSecOps practices, observability, and resilience engineering. Excellent leadership, communication, stakeholder management, and mentoring skills. Benefits: Competitive salary and benefits package Culture focused on talent development with quarterly growth opportunities and company-sponsored higher education and certifications Opportunity to work with cutting-edge technologies Employee engagement initiatives such as project parties, flexible work hours, and Long Service awards Annual health check-ups Insurance coverage: group term life, personal accident, and Mediclaim hospitalization for self, spouse, two children, and parents Values-Driven, People-Centric & Inclusive Work Environment: Persistent is dedicated to fostering diversity and inclusion in the workplace. We invite applications from all qualified individuals, including those with disabilities, and regardless of gender or gender preference. We welcome diverse candidates from all backgrounds. We support hybrid work and flexible hours to fit diverse lifestyles. Our office is accessibility-friendly, with ergonomic setups and assistive technologies to support employees with physical disabilities. If you are a person with disabilities and have specific requirements, please inform us during the application process or at any time during your employment. Let's unleash your full potential at Persistent - persistent.com/careers "Persistent is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind."

Persistent — 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.

Open Resource ↗
COMPANY GUIDE 🎯
Persistent Interview Preparation Corner

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

Open Resource ↗
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.

Open Resource ↗
CODING PRACTICE 💻
Algorithm and Data Structure Problem Set

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

Open Resource ↗
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 (Angular, Full Stack, Artificial Intelligence, Java, React.js, Stack) & 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
Persistent 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 Persistent as a Lead AI Full Stack Engineer?
Preparation Tip: Highlight Persistent's market reputation, recent tech innovations, and how your skills in Angular 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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