Tech Lead – .NET (Lending)

Job Summary We are seeking a hands-on Tech Lead to spearhead the development and delivery of our core lending modules. This is a high-impact role requiring a rare blend of deep technical coding expertise and engineering leadership. You will be responsible for translating high-level architectural blueprints into robust, production-grade code while ensuring the day-to-day technical health of the team. As a Tech Lead, you aren't just a manager; you are the technical anchor who ensures that every pull request, every database schema, every database query and every API contract meets the highest standards of performance, scalability, security, and maintainability. Key Responsibilities 1. Implementation & Low-Level Design (LLD)  Feature Delivery: Lead the development of lending workflows, features and modules (e.g., automated credit decisioning, income assessment engines, and KYC integrations).  Domain-Driven Design (DDD): Apply Strategic Mapping and Tactical Patterns (Aggregates, Entities, Domain services etc) to decouple business logic from the persistence layer.  Code Quality & SQL review: Set the standard for clean code. Conduct rigorous code reviews covering both C# logic and SQL query plans and implement automated testing strategies (eg. Unit, Integration, and Contract testing).  Defensive Engineering: Build robust error-handling and "Retry" frameworks to prevent workflows from breaking during transient network or DB glitches.  Performance Engineering: Identify and fix bottlenecks in real-time. Optimize LINQ queries, manage memory leaks in .NET, and fine-tune SQL/NoSQL performance.  Architectural Realization: Take the HLD from the Architect / Tech COE and break it down into detailed class diagrams, sequence diagrams, and API specifications. PUBLIC 2. Database Remediation & Refactoring  Logic Migration: Lead the strategy to extract complex business logic from legacy Stored Procedures and re-implement it into testable, scalable .NET Domain Services.  Schema Modernization: Fix "Null Value" inconsistencies and data integrity issues by enforcing strict schema-first designs and DTO validation layers.  Advanced Indexing: Partner with the traditional DBA to implement sophisticated indexing strategies and partition large lending datasets for sub-second retrievals. 3. Engineering Leadership & Mentorship  Team Management: Lead a squad of 5–10 developers, ensuring they are technically unblocked and following the established patterns (eg. GoF, SOLID).  Technical Debt Management: Balance the need for speed with the necessity of quality. Proactively identify and refactor technical debt before it impacts delivery.  Culture of Excellence: Foster a DevOps and FTR mindset within the team, promoting CI/CD best practices and “zero-defect” culture. 4. Stability & Day-2 Day Operations  Observability: Implement logging, monitoring, and alerting (APM tools) to ensure 24/7 stability of the lending platform.  Production Support: Act as the final escalation point for complex production incidents, "Production Fire drills" related to high DB sessions, deadlocks, and long-running queries. performing root cause analysis (RCA) and ensuring long-term fixes. 5. AI-Augmented Engineering & Implementation  AI Productivity Leadership: Drive the adoption of AI coding assistants (e.g., GitHub Copilot or Gemini Code Assist) within the squad to accelerate boilerplate generation, unit testing, and documentation.  AI Code Governance: Establish and enforce rigorous "Human-in-the-Loop" review standards for AI-generated code to ensure it meets SOLID principles, security guardrails, and performance benchmarks.  AI-Assisted Modernization: Leverage AI tools to assist in the deconstruction and refactoring of legacy .NET code and T-SQL Stored Procedures into modern microservices. PUBLIC  Prompt Engineering for DevEx: Curate a shared library of optimized prompts for the team to standardize tasks like log analysis, bug reproduction, and API contract generation.  AI Feature Implementation: Partner with the Lead Architect to implement AI-driven features (e.g., automated income verification or fraud triggers), ensuring high-quality integration between LLM outputs and core lending logic Required Technical Expertise The .NET Stack (Expert Level)  Modern .NET: Deep mastery of .NET 6/7/8, ASP.NET Core Web API, and C# internals.  Data Access: Advanced experience with EF Core and Dapper; ability to write and optimize complex SQL stored procedures and triggers for financial ledgering.  Resiliency Patterns: Hands-on experience with Polly (Retries, Circuit Breakers) and decoupled processing  Concurrency: Expert-level understanding of multi-threading and async/await patterns to handle high-concurrency lending transactions. Architecture & Integration  Microservices: Practical experience building and deploying microservices in a containerized environment and using the Strangler Fig Pattern to decompose a monolith  Messaging: Hands-on experience with RabbitMQ, Kafka, or Azure Service Bus for eventdriven communication.  Distributed Systems: Understanding of Idempotency (crucial for payments) and the Saga Pattern for distributed transactions. Database & Data Layer  Advanced SQL Engineering: Mastery of Execution Plans, DMV analysis, and Profiler. Proven ability to resolve blocking, deadlocks, and contention in high-concurrency environments.  Transaction Integrity: Expert knowledge of local vs. distributed transactions and how to handle high-concurrency and deep understanding of Transaction Isolation Levels and implementing Idempotency at the database level to ensure safe retries. PUBLIC  ORM Mastery: Deep expertise in EF Core and Dapper, specifically knowing when to use which to avoid performance overhead.  Data Lifecycle: Experience in partitioning large tables (Millions of records) and designing archival strategies to keep the "Hot" database lean Qualifications & Experience  Experience: 12–16 years in software development, with at least 3+ years in a Tech Lead capacity in a high-transaction environment (Fintech/Lending preferred)  Hands-on Coding: Must be willing to code 50–60% of the time.  Agile: Expert in Scrum/Kanban, with a track record of delivering complex features in 2week sprints.  AI familiarity: Familiarity with AI-augmented development tools (Copilots), prompt engineering, and the implementation of AI-driven features within microservices will be a significant plus  Education: Bachelor’s or Master’s in Computer Science, Software engineering

Pay: ₹2,000,000.00 - ₹2,260,000.00 per year

Work Location: In person