L #3140
Applications open
Full-Time Job
AI/ML Engineer — Generative AI & LLMs
Shodh AI
About the role
About the roleShodh AI is hiring an AI/ML Engineer to help build enterprise AI applications using open-weight language models. You will work on document understanding, structured information extraction, model fine-tuning and evaluation.
This is a hands-on engineering role. You will own clearly scoped tasks, supported by our senior technical team for architecture, complex troubleshooting and deployment decisions.
What you’ll do
- Prepare, clean and validate datasets, including synthetic training examples.
- Adapt language models using supervised fine-tuning and techniques such as LoRA/QLoRA.
- Develop prompts and pipelines for extracting business rules and producing structured outputs.
- Build tests to evaluate correctness, consistency and handling of ambiguous or missing information.
- Integrate models with application APIs alongside backend engineers.
- Help package and deploy models in GPU-backed, on-premises and offline environments.
- Document experiments, results and known limitations.
What we’re looking for
- 1–2 years of relevant experience in AI/ML engineering.
- Strong Python programming and debugging skills.
- Practical experience with PyTorch and Hugging Face Transformers.
- Hands-on experience building an LLM application, fine-tuning a model or deploying an inference service.
- Understanding of training, validation and test datasets.
- Familiarity with Git and Linux.
- Clear communication, curiosity and a willingness to learn.
Good to have
- Exposure to PEFT, LoRA/QLoRA, TRL or vLLM.
- Experience with document processing, JSON outputs and schema validation.
- Familiarity with Docker, quantization or GPU inference.
How to apply
- Apply through LinkedIn with your CV and a relevant project link or brief project description. Please explain what you personally built. You do not need to share confidential code or data.
Eligibility & process
Who can apply
Seniority: Entry level. Employment type: Full-time
Skills & stack
Selection process
Details are shared by the recruiter after shortlisting.
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 Shodh AI and tech roles.
Technical Placement 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
Shodh AI Interview Preparation Corner
Comprehensive guide covering typical interview formats, common questions, and preparation tips for Shodh AI 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 (Python, Docker, Git, Linux, Machine Learning, Generative AI / LLMs, PyTorch, TensorFlow) & 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
Shodh AI 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 Shodh AI as a AI/ML Engineer — Generative AI & LLMs?
Highlight Shodh AI's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Highlight Shodh AI'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.
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.
Competitive Package
Jaipur, India
Seniority: Entry level. Employment type: …
