N #2424
Applications open
Full-Time Job
Python AI Engineer
Ascendion Engineering
About the role
About Ascendion Ascendion is a leader in AI-Native platform driven software engineering company that underwrites outcomes, helping businesses innovate faster, smarter, and with greater impact. We partner with Global 2000 clients across North America, UK, Europe, and APAC to solve complex challenges in data, experience design, software product engineering, and workforce transformation. Powered by expert engineers, thousands of AI agents, and our Engineering to the Power of AIโข (EngineeringAI) method, we deliver measurable outcomes that build trust, unlock value, and accelerate growth. Learn more at ascendion.com Ascendion | Engineering to elevate life We have a culture built on opportunity, inclusion, and a spirit of partnership. Come, change the world with us: - Build the coolest tech for world's leading brands - Solve complex problems โ and learn new skills - Experience the power of transforming digital engineering for Fortune 500 clients - Master your craft with leading training programs and hands-on experience Experience a community of change makers! Join a culture of high-performing innovators with endless ideas and a passion for tech. Our culture is the fabric of our company, and it is what makes us unique and diverse. The way we share ideas, learning, experiences, successes, and joy allows everyone to be their best at Ascendion. About the Role We are seeking a highly motivated AI Engineer with hands-on experience in developing and deploying agentic AI architectures. The ideal candidate is proficient in Python, has worked with frameworks like LangGraph, and understands the practical implementation of AI/ML solutions. Experience in machine learning or deep learning is a plus but not required. Job Title: Python AI Engineer Key Responsibilities: - Design, build, and deploy agentic AI systems, leveraging modern frameworks. - Develop and maintain scalable AI workflows using LangGraph and related tools. - Collaborate with cross-functional teams to integrate AI agents into real-world applications. - Write clean, efficient, and testable Python code. (Optional) Apply data science techniques to train and fine-tune ML or deep learning models. Minimum Qualifications: -Strong proficiency in Python and related libraries. - Experience with agentic AI architectures and autonomous AI agents. - Hands-on experience with LangGraph or similar orchestration frameworks. - Understanding of AI lifecycle: prompt engineering, agent workflows, deployment, and monitoring. - Familiarity with LLMs and multi-agent systems. Location: Bangalore Want to change the world? Let us know. Tell us about your experiences, education, and ambitions. Bring your knowledge, unique viewpoint, and creativity to the table. Let's talk!
Eligibility & process
Who can apply
Experience: 3-5 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 Ascendion Engineering 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
Ascendion Engineering Interview Preparation Corner
Comprehensive guide covering typical interview formats, common questions, and preparation tips for Ascendion Engineering 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 (SQL, JavaScript, Architecture, CSS3, Processing, Microsoft, Natural Language Processing, Python) & 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
Ascendion Engineering 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 Ascendion Engineering as a Python AI Engineer?
Highlight Ascendion Engineering's market reputation, recent tech innovations, and how your skills in SQL directly solve their team's objectives.
Highlight Ascendion Engineering's market reputation, recent tech innovations, and how your skills in SQL 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)
Full-Time Job
India (Remote / Multiple), India
Nov 07, 2026
