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Applications open Full-Time Job

Principal Software Engineer

Orcapod Consulting Services

  • Pune, India
  • 65-75 Lacs PA
  • 12-20 Yrs
  • Posted Today
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About the role

We are looking for a Principal Software Engineer to spearhead the architecture, evolution, and delivery of iModel Cloud Sync Services, enabling high-volume data ingestion, transformation, and synchronization from heterogeneous engineering sources into the iTwin platform. This is a hands-on technical leadership role requiring deep expertise in cloud-native architecture, distributed systems, orchestration frameworks, state-machine-driven workflows, and enterprise-grade reliability. As a Principal, you will define architectural direction, mentor teams, and ensure that the platform is engineered for scale, resilience, and long-term sustainability.

Responsibilities

  • Architectural Leadership Evolve end-to-end architecture for cloud-based ETL workflows and engineering data synchronization.
  • Architect event-driven systems using microservices, container orchestration, and state-machine-driven execution.
  • Lead design of multi-tenant services optimized for global scale, performance, and cost-efficiency.
  • Cloud Platform & Distributed Systems
  • Design cloud-native pipelines using Kubernetes, focusing on zero-downtime rollouts and secure configuration management.
  • Implement distributed locking and conflict-resolution mechanisms for high-concurrency data sync.
  • Ensure robust observability through distributed tracing, automated diagnostics, and structured logging.
  • Data & ETL Workflow Architecture Drive ETL design for engineering data, focusing on schema management, versioning, and domain-model mapping.
  • Master workflow orchestration using engines like Temporal or Step Functions to ensure idempotency and transactional integrity.
  • Reliability, Resilience & Operational Excellence Define resilience strategies, including SLOs, self-healing workflows, circuit breakers, and failure isolation patterns.
  • Champion operational health through architecture reviews, capacity planning, and cloud cost governance.
  • Technical Leadership & Mentorship
  • Mentor senior engineers on distributed systems, cloud-native patterns, and engineering best practices.
  • Align engineering execution with long-term platform vision by partnering with product and architecture leaders.

Eligibility & process

Who can apply

Experience: 12-20 Yrs. Open to matching qualifications.

Skills & stack

C# Containerization Microservices .NET Core RabbitMQ Cloud Operations Data Workflow

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 (C#, Containerization, Microservices, .NET Core, RabbitMQ, Cloud Operations, Data, Workflow) & 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 Orcapod Consulting Services 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 Orcapod Consulting Services as a Principal Software Engineer?
Highlight Orcapod Consulting Services's market reputation, recent tech innovations, and how your skills in C# 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.
Orcapod Consulting Services 65-75 Lacs PA
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