KASHII UPDATEZ Everyday Student Requirements & Python Coding Tutorials by Python Kashi
REQUIREMENT_ID_1509 โ€ข 3-DAY_ACTIVE_POLICY

GenAI Engineer

Company
Company Workiy
Type
Opportunity Type Internship
Salary
Stipend / Salary {'@type': 'QuantitativeValue', 'value': '', 'unitText': 'YEAR'}
Location
Location Remote
Posted Date
Posted Date Today
Generative AI Large Language Models Prompt Engineering Python REST APIs AI/ML fundamentals API integration Software development Problemโ€‘solving Analytical skills Cloud platforms
๐Ÿ“–
Placement Paper Practice Open Resource โ†—
Helps candidates prepare for technical interviews and coding challenges relevant to AI roles at Workiy.
๐Ÿ“–
AI and ML Concepts Review Open Resource โ†—
Provides foundational knowledge on AI and machine learning topics useful for the GenAI Engineer role.
๐Ÿ“–
Career Guidance for Tech Roles Open Resource โ†—
Offers insights into career paths and skill development for aspiring AI engineers.
๐Ÿ“–
Coding Practice Problems Open Resource โ†—
A collection of algorithmic problems to sharpen coding skills required for technical interviews.

Bachelorโ€™s degree in Computer Science, Engineering, or a related field with a minimum of 60% or 6.5 CGPA, no backlogs. Candidates with experience in AI/ML, Python programming, and REST API development are preferred. Strong analytical and problemโ€‘solving skills are essential.

1
Round 1: Technical interview covering coding and AI concepts
2
Round 2: System design / AI project discussion
3
Round 3: HR interview
Workiy is a forwardโ€‘thinking technology startup that specializes in delivering AIโ€‘driven solutions to streamline business processes and enhance customer experiences. With a remoteโ€‘first culture, the company empowers its engineers to collaborate across time zones while maintaining a flexible workโ€‘life balance. Workiyโ€™s product portfolio spans intelligent chatbots, automated content generation, and dataโ€‘centric decisionโ€‘support tools, all built on cuttingโ€‘edge generative AI and large language models. The GenAI Engineer role is a contractโ€‘based, remote position focused on designing, developing, and deploying generative AI features that solve realโ€‘world business challenges. The successful candidate will work closely with product managers, data scientists, and backend developers to translate business requirements into scalable AI solutions. The role demands a blend of deep technical expertise in LLMs, prompt engineering, and software engineering, along with a passion for experimentation and continuous improvement. Key Responsibilities: 1. Design and implement generative AI models and pipelines that meet product specifications. 2. Build and expose RESTful APIs to integrate AI services into existing applications. 3. Experiment with different LLMs, prompt strategies, and fineโ€‘tuning techniques to optimize output quality. 4. Collaborate with data teams to curate highโ€‘quality training datasets. 5. Conduct rigorous testing, evaluation, and A/B experiments to validate model performance. 6. Troubleshoot and optimize inference latency and resource utilization. 7. Document model architecture, data flows, and best practices for internal knowledge sharing. 8. Stay updated on the latest research in generative AI and propose innovative use cases. 9. Mentor junior engineers and interns on AI fundamentals and coding standards. 10. Ensure compliance with data privacy and security guidelines. Tech Stack: Python, PyTorch/TensorFlow, Hugging Face Transformers, OpenAI API, Docker, Kubernetes, AWS/GCP/Azure, REST APIs, Git, CI/CD pipelines. Growth Path: Starting as a GenAI Engineer, you can progress to Senior AI Engineer, Lead AI Architect, or Product Manager for AI solutions. The remote environment encourages continuous learning and crossโ€‘functional collaboration, enabling rapid skill acquisition and career advancement. Why Join Workiy: Workiy offers the unique opportunity to shape the future of AI in business, work with a highly skilled, globally distributed team, and enjoy the freedom of a remote contract role. The startup environment fosters innovation, rapid iteration, and a culture that values ownership and impact. If you are eager to push the boundaries of generative AI and build products that matter, Workiy is the place to be.

Workiy โ€” AI & Machine Learning Interview Guide

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

APTITUDE & LOGIC ๐Ÿ“–
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 โ†—
COMPANY GUIDE ๐ŸŽฏ
Workiy Interview Preparation Corner

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

Open Resource โ†—
MOCK TESTS & PAPERS ๐Ÿ“
Comprehensive Interview Prep Resources & Syllabus

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

Open Resource โ†—
CODING PRACTICE ๐Ÿ’ป
Algorithm and Data Structure Problem Set

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

Open Resource โ†—
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 (Generative AI, Large Language Models, Prompt Engineering, Python, REST APIs, AI/ML fundamentals, API integration, Software development, Problemโ€‘solving, Analytical skills, Cloud platforms) & 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
Workiy 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 โ–ผ
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 โ–ผ
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 โ–ผ
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 โ–ผ
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 Workiy as a GenAI Engineer?
Preparation Tip: Highlight Workiy's market reputation, recent tech innovations, and how your skills in Generative AI 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.

More Fresh Requirements in Software Engineering

View Category Feed โ†—
Workiy
GenAI Engineer
Apply Apply Now โ†—
Chat Chat with Kashii