N #3219
Applications open
Full-Time Job
AI/ML
Infosys
About the role
Job Summary
We are looking for an experienced AI/ML Engineer with 2-5 years of industry experience in developing, deploying, and optimizing Machine Learning and Generative AI solutions. The ideal candidate should possess strong expertise in machine learning algorithms, deep learning, NLP, LLMs, model deployment, and MLOps practices.
Key Responsibilities
- Design, develop, and deploy Machine Learning and Deep Learning models for business use cases.
- Build and optimize AI/ML pipelines for data processing, training, evaluation, and deployment.
- Develop Generative AI applications using Large Language Models (LLMs).
- Fine-tune foundation models using frameworks such as Hugging Face and Lang Chain.
- Implement NLP solutions including text classification, sentiment analysis, summarization, and conversational AI.
- Collaborate with data engineers, product managers, and business stakeholders to translate requirements into AI solutions.
- Monitor model performance, accuracy, and drift in production environments.
- Create scalable AI solutions on cloud platforms such as Azure, AWS, or GCP.
- Implement MLOps best practices for model lifecycle management.
- Stay current with advancements in AI, Generative AI, and Machine Learning technologies.
Required Skills
- Strong experience in Python and AI/ML libraries such as NumPy, Pandas, Scikit-learn.
- Hands-on experience with TensorFlow, PyTorch, Keras , or similar frameworks.
- Experience in Machine Learning, Deep Learning, NLP, and Generative AI .
- Strong understanding of LLMs, Prompt Engineering, RAG, Vector Databases , and AI Agents.
- Experience with LangChain, LlamaIndex, Hugging Face , or similar frameworks.
- Knowledge of MLOps tools such as MLflow, Kubeflow, SageMaker, or Azure ML.
- Experience working with SQL and NoSQL databases.
- Understanding of data structures, algorithms, and software engineering best practices.
- Strong analytical and problem-solving skills.
Preferred Skills
- Experience with Azure OpenAI, OpenAI APIs, Gemini, Claude, or similar AI platforms.
- Hands-on experience in AI Agent development and orchestration frameworks.
- Knowledge of Docker, Kubernetes, and CI/CD pipelines.
- Experience with Vector Databases such as Pinecone, Weaviate, ChromaDB, or FAISS.
- Exposure to Computer Vision and multimodal AI solutions.
- Educational Qualification Bachelor's or master's degree in computer science, Artificial Intelligence, Data Science, Information Technology, or related fields.
- Nice to Have Microsoft Azure AI Engineer Associate (AI-102) AWS Machine Learning Specialty Google Professional Machine Learning Engineer Experience working in enterprise-scale AI transformation programs.
- Keywords
Eligibility & process
Who can apply
Experience: 2-5 Yrs. Open to matching qualifications.
Skills & stack
Selection process
- Online Assessment / Technical Interview Rounds / HR Review
Prep kit
20 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 Infosys 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
Infosys Interview Preparation Corner
Comprehensive guide covering typical interview formats, common questions, and preparation tips for Infosys 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, Python, Machine Learning, Machine, ETL, Aiml, SQL, AWS) & 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.
Technical questions 5
What is the difference between WHERE and HAVING clauses in SQL? Answer
Model answer: WHERE filters rows before any groupings are applied, while HAVING filters aggregated groups after GROUP BY has executed.
Explain SQL Window functions: ROW_NUMBER(), RANK(), and DENSE_RANK(). Answer
Model answer: ROW_NUMBER() assigns unique sequential integers. RANK() assigns identical ranks to ties and skips ranks. DENSE_RANK() assigns identical ranks to ties without skipping rank numbers.
How do you handle NULL and missing values during data cleaning in Python/Pandas? Answer
Model answer: Use .isna().sum() to identify missing values. Impute with mean/median using .fillna() or remove with .dropna(subset=[...]) depending on variance impact.
What is the difference between Star Schema and Snowflake Schema in Data Warehousing? Answer
Model answer: Star Schema has denormalized dimension tables directly connected to the central Fact table. Snowflake Schema normalizes dimension tables into sub-dimensions to minimize redundancy.
How do you calculate MoM (Month-over-Month) growth in SQL? Answer
Model answer: Use LAG(revenue, 1) OVER (ORDER BY month) to fetch the previous month's revenue and compute (revenue - prev_revenue) / prev_revenue * 100.
Behavioural & HR questions 3
Why do you want to join Infosys as a AI/ML?
Highlight Infosys's market reputation, recent tech innovations, and how your skills in Artificial Intelligence directly solve their team's objectives.
Highlight Infosys'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.
Competitive Package (Best in Industry)
Bengaluru, India
Experience: 2-5 Yrs. Open to matching quaโฆ
