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AI Backend Engineer

techolution

  • India (Remote / Multiple), India
  • Competitive Package
  • 1-4 Years
  • Posted Today
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About the role

We are looking for a AI Backend Engineer with strong Python backend development skills and practical exposure to Generative AI. The role focuses on developing scalable microservices, building reusable software components, and integrating AI agents and RAG-based workflows into intelligent applications.

You will contribute to AI products helping transform user requirements into structured knowledge workflows, reusable AI agents, and chatbot capabilities.

The ideal candidate has a strong foundation in Python, Object-Oriented Programming, API development, and problem-solving, along with hands-on exposure to LLMs, RAG, embedding models, vector databases, and AI agent development.

Title : AI Backend Engineer

Location :Remote Work

Employment Type: Full-time

Exp Range : 1-4 years

Key Responsibilities

  • Python Backend Development: Develop, maintain, and optimize backend services and REST APIs using Python and FastAPI/Flask.
  • Microservices Development: Build modular, reusable, and maintainable services using Object-Oriented Programming principles and established engineering standards.
  • Data Modeling and Validation: Design data models, request/response schemas, and validation logic using Pydantic and appropriate database technologies.
  • AI Agent Development: Build, customize, and integrate AI agents for different business use cases, following established engineering standards.
  • RAG and Knowledge Retrieval: Develop and integrate Retrieval-Augmented Generation Workflows using LLMs, embedding models, and vector databases.
  • Agent Orchestration: Support the integration and coordination of multiple agents and backend services where required.
  • Database and Caching: Work with NoSQL databases, data modeling, and Redis or other caching mechanisms to support efficient application performance.
  • Asynchronous Programming: Use Python asynchronous programming where appropriate for API calls, database operations, and LLM requests.
  • Debugging and Optimization: Investigate application issues, analyze logs, identify root causes, and improve service reliability and performance.
  • Cloud and Deployment Support: Work with GCP or AWS environments to assist with deployment troubleshooting, log analysis, and application monitoring.

Qualifications

  • Strong programming skills in Python and a solid understanding of software development fundamentals.
  • Hands-on experience developing backend applications and REST APIs using FastAPI or Flask.
  • Strong understanding of Object-Oriented Programming (OOP), modular design, and reusable components.
  • Experience with Pydantic or similar frameworks for data validation and serialization.
  • Understanding of microservices architecture and service-to-service communication.
  • Strong problem-solving, analytical, and debugging skills.
  • Practical exposure to Generative AI technologies, including LLMs, RAG, embedding models, vector databases, and LLM/API integrations.
  • Basic hands-on exposure to AI agents and orchestration, with an understanding of how agents can be developed or adapted for specific use cases.
  • Understanding of data modeling and NoSQL databases.
  • Familiarity with Redis and caching concepts.
  • Understanding of asynchronous programming in Python, including async/await.
  • Good understanding of Git and version control.
  • Exposure to GCP or AWS, including basic deployment troubleshooting and log analysis.

About Techolution

Techolution is the world’s leading FEA – Full Enterprise Automation specialist. We are trusted by boards and executive leaders who need AI with real business outcomes. Despite Gen AI, LLMs, and billions in investment, most enterprise AI programs aren't delivering results.

Techolution fixes that with BPA 4.0. We take the power of big Tech’s Generic AI and multiply it with our Expert AI stack — resulting in business process automations that follow the same nuances as domain experts do on the job at an unprecedented speed.

Our platform has delivered over $900M in AI ROI for our clients in less than a year, all while increasing their AI Adoption by 10x.

Our 3 power moves for ROI from AI:

  • Value Finder - We Identify high-impact AI and automation opportunities in enterprises and build solutions that deliver measurable value in as little as 30 days.
  • 100K Challenge: Work on fast-paced AI initiatives where teams build solutions designed to generate 2× ROI within 100 days.
  • GE LEAP: Contribute to enterprise AI transformation through our partnership with Google, helping scale Gemini-driven solutions across organizations.

Visit us @www.techolution.com : To know more about our revolutionary core practices and getting to know in detail about how we enrich the human experience with technology.

Eligibility & process

Who can apply

Seniority: Associate. Employment type: Full-time

Skills & stack

Python FastAPI Flask REST APIs Microservices Redis AWS GCP

Selection process

Details are shared by the recruiter after shortlisting.

Prep kit

19 resources
Study material 4
Company-specific guides 4
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, FastAPI, Flask, REST APIs, Microservices, Redis, AWS, GCP) & 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 techolution 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 techolution as a AI Backend Engineer?
Highlight techolution'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).
How do you handle strict deadlines or sudden scope changes?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
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