N #2191
Applications open
Full-Time Job
Senior Artificial Intelligence Engineer
Yotta Infrastructure
About the role
Job Scope: The research, design, and development of advanced AI and ML models powering a cutting-edge AI-driven no-code development platform and a scalable AI inference and training orchestration system. Responsible for building scalable ML pipelines, optimizing models for production, mentoring team members, and translating research innovations into impactful product features aligned with business goals. Job Responsibilities: Design and implement state-of-the-art machine learning and deep learning models for NLP, computer vision, and generative AI relevant to no-code AI coding and AI orchestration platforms. Develop, optimize, and fine-tune large-scale models, including transformer based architectures and generative models. Architect and manage end-to-end machine learning pipelines: data processing, training, evaluation, deployment, and continuous monitoring. Collaborate closely with software engineering teams to productionize models ensuring reliability, scalability, and performance. Research and integrate cutting-edge AI techniques and algorithms to maintain product competitiveness. Maintain up-to-date knowledge of AI research trends and technologies, assessing their applicability. Ensure compliance with data privacy and security standards in AI model development. Good to have skills: Experience with AI-driven no-code platforms or automated code generation. Familiarity with AI workflow orchestration frameworks like LangChain, Crew AI, or similar. Knowledge of probabilistic modeling and uncertainty quantification. Hands-on experience with MLOps tools and practices including CI/CD, model versioning, and monitoring. Familiarity with cloud platforms (AWS, GCP, Azure) and container orchestration (Docker, Kubernetes). Contributions to open-source AI projects or patent filings. Understanding of AI ethics, data privacy (GDPR, SOC2) compliance. Strong academic research background with publications in top-tier AI/ML conferences. Qualification and Experience: B.E/ B.Tech, Computer Science, Electrical Engineering, Statistics, Mathematics, or related fields with a specialization in Artificial Intelligence, Machine Learning, or Deep Learning. 7 plus years of experience. Strong research publication record in reputed AI/ML conferences (NeurIPS, ICML, ICLR, CVPR, ACL). Demonstrated experience in developing and deploying deep learning models including transformers, CNNs, RNNs, GNNs, and generative AI models. Proven skills in NLP and/or computer vision. Hands-on experience with Python and ML frameworks such as PyTorch, TensorFlow, JAX. Experience building scalable ML pipelines and applying MLOps best practices. Knowledge of distributed training, GPU acceleration, and cloud infrastructure is highly desirable. Excellent problem-solving, analytical, and communication skills. Interested candidates can share their updaetd resume at sadhatrao@yotta.com Regards, YOTTA
Eligibility & process
Who can apply
Experience: 7-12 Yrs. Open to matching qualifications.
Skills & stack
Selection process
- Online Assessment / Technical Interview Rounds / HR Review
Prep kit
19 resourcesStudy material 4
Aptitude Practice Questions & Mock Tests
Curated logical, quantitative, and verbal reasoning problems for the initial online screening round.
Company-Specific Interview Preparation Corner
Detailed interview experiences, exam formats, and previous test questions for Yotta Infrastructure and tech roles.
Technical Placement Cheat Sheets & Question Bank
High-yield coding cheat sheets, core CS fundamentals (OOP, DBMS, OS, Networks), and rapid revision guides.
Algorithm, DSA & Live Code Debugger Practice
Hands-on problem sets to improve coding speed and step-by-step memory debugging.
Company-specific guides 4
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.
COMPANY GUIDE
Yotta Infrastructure Interview Preparation Corner
Comprehensive guide covering typical interview formats, common questions, and preparation tips for Yotta Infrastructure and off-campus tech roles.
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.
CODING PRACTICE
Algorithm and Data Structure Problem Set
Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.
Likely coding questions 4
Round-wise strategy 4
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 (Artificial Intelligence, Languages, Intelligence, AWS, Ml, Senior, Machine, Machine learning) & 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
Yotta Infrastructure Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 4
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).
Behavioural & HR questions 3
Why do you want to join Yotta Infrastructure as a Senior Artificial Intelligence Engineer?
Highlight Yotta Infrastructure's market reputation, recent tech innovations, and how your skills in Artificial Intelligence directly solve their team's objectives.
Highlight Yotta Infrastructure's market reputation, recent tech innovations, and how your skills in Artificial Intelligence directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
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?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.