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Applications open Full-Time Job

Cloud & AI Engineer

Sonata Software

  • Bengaluru, India
  • 9-19 Lacs PA
  • 6-9 Yrs
  • Posted Today
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About the role

Job Description

Mandatory Skills Artificial Intelligence,AWS,Microsoft Azure

Job Description

6+ of overall experience in software engineering, cloud engineering, AI/ML engineering, or related areas. Strong production experience with both AWS and Azure . Strong hands-on serverless development and architecture experience.

Experience delivering production-grade AI/GenAI applications . Experience designing event-driven and distributed cloud architectures. Strong troubleshooting, system-design, and technical leadership capabilities.

AI & Generative AI Amazon Bedrock and/or Azure OpenAI. Azure AI Foundry and Azure AI Search. Amazon SageMaker and/or Azure Machine Learning.

LLMs, embeddings, vector search, RAG, prompt engineering, and AI agents. Integration of LLM applications with Lambda/Azure Functions . Serverless RAG and event-driven GenAI workflows.

LLM evaluation, observability, guardrails, and responsible AI. Programming & Development Strong Python programming skills. Experience developing REST APIs using FastAPI, Flask, or equivalent.

Strong knowledge of JSON, REST, authentication, OAuth/OIDC, and API integration. Experience with asynchronous programming and distributed systems. Strong understanding of software engineering principles, design patterns, and clean code.

Cloud, DevOps & MLOps Strong hands-on experience with AWS and Azure . Docker and Kubernetes experience. Terraform, AWS CDK/SAM, or Azure Bicep.

CI/CD using GitHub Actions, Azure DevOps, Jenkins, GitLab, or equivalent. MLOps/LLMOps using MLflow, SageMaker, Azure ML, or equivalent. Monitoring and observability using CloudWatch, Azure Monitor, Application Insights, OpenTelemetry, or equivalent.

Security AWS IAM and Azure RBAC/Entra ID. Secrets management using AWS Secrets Manager and Azure Key Vault. Encryption at rest and in transit.

VPC/VNet, private endpoints, security groups, and network isolation. API authentication, authorization, throttling, and secure API design. Understanding of enterprise AI security and data privacy.

Eligibility & process

Who can apply

Experience: 6-9 Yrs. Open to matching qualifications.

Skills & stack

Devops Kubernetes Cloud Jenkins AWS Microsoft Azure Microsoft Python

Selection process

  1. Online Assessment / Technical Interview Rounds / HR Review

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 (Devops, Kubernetes, Cloud, Jenkins, AWS, Microsoft Azure, Microsoft, 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 Sonata Software 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 Sonata Software as a Cloud & AI Engineer?
Highlight Sonata Software's market reputation, recent tech innovations, and how your skills in Devops 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.
Sonata Software 9-19 Lacs PA
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