N #3495
Applications open
Full-Time Job
AI Solutions Engineer
Ascendion Engineering
About the role
Job Title: AI
Solutions Engineer Job Type
Full-Time Work Mode: Hybrid (2/3 Days Office)
Location
Hyderabad, Bengaluru, Chennai,
Pune Experience
6 - 12 Years Working Hours: 2 - 11 PM
About the Role
We are seeking a highly skilled AI Solutions Engineer to architect, develop, and deploy enterprise-grade AI solutions using Generative AI, Large Language Models (LLMs), Python, .NET/C#, and Microsoft Azure Cloud as the primary technology stack. The ideal candidate will possess strong AI engineering expertise combined with modern software engineering capabilities to build scalable, secure, and production-ready AI applications. This role requires hands-on experience designing and implementing LLM-powered applications, RAG solutions, Agentic AI systems, and cloud-native AI platforms .
The successful candidate will be responsible for translating business requirements into enterprise AI architectures while owning the complete solution lifecycle from design and development through deployment, optimization, and production support.
Key Responsibilities
- Design, build, and deploy enterprise-scale AI applications using Python, .NET, C#, REST APIs, and Microservices .
- Develop Generative AI, LLM, RAG, and Agentic AI solutions , including prompt engineering, tool/function calling, workflow orchestration, memory management, and multi-agent architectures.
- Build and optimize retrieval pipelines encompassing document ingestion, chunking, embeddings, vector databases, semantic/hybrid search, reranking, and grounded response generation.
- Leverage Azure OpenAI, Azure AI Search, Azure AI Foundry, Azure AI Services, Azure Functions, and Azure App Services to deliver scalable cloud-native AI solutions.
- Integrate AI capabilities with enterprise applications, APIs, databases, data platforms, and business workflows.
- Design secure, resilient, and observable architectures utilizing CI/CD, automated testing, monitoring, logging, and performance optimization.
- Troubleshoot and optimize AI applications across prompts, LLMs, retrieval systems, agents, APIs, backend services, and cloud infrastructure.
- Implement Responsible AI, security, governance, and engineering best practices throughout the AI solution lifecycle.
Required Skills
- AI Engineering & GenAI Strong hands-on experience building and deploying production-grade AI applications .
- Deep expertise in Generative AI, Large Language Models (LLMs), Prompt Engineering, Retrieval-Augmented Generation (RAG), and Agentic AI .
- Experience designing and implementing Multi-Agent Systems and AI orchestration workflows.
- Strong understanding of LLM evaluation, context engineering, grounding techniques, hallucination mitigation, and Responsible AI practices.
- Programming & Software Engineering Strong development experience with Python, .NET, and C# .
- Experience building enterprise applications using REST APIs, Microservices, and distributed architectures .
- Experience integrating AI services into enterprise-grade software platforms.
- Azure AI & Cloud Hands-on experience with: Azure OpenAI Service Azure AI Search Azure AI Foundry Azure AI Services Azure Functions Azure App Services Azure DevOps Experience designing, deploying, and supporting AI workloads on Microsoft Azure Cloud .
- LLM & AI Frameworks Hands-on experience with LangGraph, LangChain, Semantic Kernel, AutoGen, or similar orchestration frameworks .
- Strong expertise in embeddings, vector databases, semantic search, hybrid retrieval, metadata filtering, reranking, and retrieval optimization.
- Experience building conversational AI, copilots, intelligent assistants, and enterprise AI solutions.
- Platform Engineering Strong understanding of SQL/NoSQL databases, Docker, containerization, cloud-native architectures, monitoring, and observability.
- Experience with CI/CD pipelines, automated testing, and production support.
Preferred Skills
- Experience with MLOps, LLMOps, AI model evaluation, monitoring, and governance frameworks .
- Experience building enterprise-scale AI platforms with high availability, security, and scalability requirements.
- Exposure to modern front-end technologies such as ReactJS or TypeScript is a plus.
- US Healthcare domain experience is desirable , including knowledge of Healthcare Payer/Provider ecosystems, Claims, Enrollment, Eligibility, Prior Authorization, Provider Data Management, FHIR/HL7 standards, and healthcare workflow modernization initiatives.
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 Ascendion Engineering 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
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 (Docker, Azure App Service, Microsoft Azure, App Support, Integration, Filtering, Cloud, Metadata) & 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 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 Ascendion Engineering as a AI Solutions Engineer?
Highlight Ascendion Engineering's market reputation, recent tech innovations, and how your skills in Docker directly solve their team's objectives.
Highlight Ascendion Engineering's market reputation, recent tech innovations, and how your skills in Docker 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: 7-12 Yrs. Open to matching quโฆ
