Website:
lplfinancial.in
Job details:
What if you could build a career where ambition meets innovation?
At LPL’s Global Capability Center, you'll find a collaborative culture where your voice matters, integrity guides every decision, and technology fuels progress. Your skills, talents, and ideas will redefine what's possible. LPL's success reflects its exceptional employees, who together pursue one noble purpose: empowering financial advisors to deliver personalized advice for all who need it. We’re proud to be expanding and reaching new heights in Hyderabad.
Join us as we create something extraordinary together.
Software Engineer – .NET/C#, Angular (AI‑Accelerated with Agentic approach, Service‑Oriented)
About The Role
We’re seeking a hands‑on Software Engineer experienced in
service‑oriented architecture (SOA) using
.NET/C# and
Angular who is passionate about building reliable, secure, and scalable applications
faster with AI‑assisted development. You will own features end‑to‑end—design, implementation, test automation, observability, deployment—and will
quantifiably improve delivery efficiency vs. traditional development by using well‑known AI tools (examples below). You’ll also uphold
excellent engineering metrics, including a consistent
say/do ratio,
first‑time‑right %, and a
sprint‑over‑sprint velocity of ~10–12 story points.
What Candidate Would Do
- Design & Build Service‑Oriented APIs
- Design domain models, contracts, and microservices using ASP.NET Core (REST/gRPC) with clean architecture and DDD principles.
- Implement secure, resilient services (retry, circuit breaker, bulkhead, idempotency)
- Develop Modern Web UIs
- Build responsive, accessible Angular front‑ends/ MFEs (Angular 16+), RxJS state management, lazy loading, and a11y best practices.
- Integrate with backend services via typed clients
- Leverage AI Developer Tools to Deliver Faster
- Use AI tools to accelerate code generation, refactoring, test authoring, documentation, and code review. Examples:
- GitHub Copilot / Copilot Chat for inline code suggestions, unit test scaffolding, and PR review comments.
- Tabnine, AWS CodeWhisperer, or Cursor/JetBrains AI Assistant for alternative code completions, refactors, and test cases.
- SonarLint/SonarQube/Snyk in AI‑assisted mode for security/code quality autofix suggestions.
- Track and present AI efficiency metrics (e.g., % AI‑assisted lines, time saved per task, fewer defects per LOC).
- Own Full SDLC with Automation
- Author high‑coverage unit tests (xUnit/NUnit), integration tests (WebApplicationFactory, Testcontainers), and end‑to‑end tests (Playwright/Cypress).
- Build CI/CD pipelines (GitHub Actions/Azure DevOps), implement quality gates (coverage, linting, SAST/DAST), and trunk‑based development with feature flags.
- Instrument services with OpenTelemetry, distributed tracing, structured logging, and SLO/SLA dashboards.
- Measure & Improve Engineering Excellence
- Maintain a say/do ratio ≥ 0.9 (committed vs. delivered).
- Achieve first‑time‑right ≥ 90% (stories completed without rework).
- Sustain velocity of ~10–12 story points per engineer per sprint.
- Monitor DORA metrics (Deployment Frequency, Lead Time, Change Failure Rate, MTTR) and code quality (defect density, maintainability index).
- Security, Compliance & Reliability
- Implement OAuth2/OIDC, OWASP ASVS, input validation, secret management (Key Vault), and zero‑trust patterns.
- Adhere to secure coding standards and perform automated security scans in CI.
- Collaboration & Communication
- Partner with Product, Platform, and QA to refine backlog items with clear acceptance criteria.
- Write concise ADRs, RFCs, and technical docs; lead design and post‑incident reviews.
Engineering Metrics & Expectations
Candidate Will Be Complying To Both Outcomes And Behaviors
Say/Do Ratio ≥ 0.9 Definition: Delivered points / Committed points per sprint. Practice: Commit based on team capacity, keep WIP low, re‑plan early if blockers arise.First‑Time‑Right ≥ 85% Definition: Stories meeting acceptance criteria without rework or defect reopening. Practice: Collaborate on ACs, write contract tests, perform self‑QA, use AI to catch edge cases.Velocity ~10–12 SP per sprint (individual) Definition: Sustainable delivery rate with stable scope. Practice: Slice work vertically, automate tests, pair program (human + AI), remove toil.Quality & FlowUnit test coverage ≥ 80% on core modules; integration/E2E tests for critical paths.DORA targets: Frequent deploys (daily/weekly), short lead time (<1–2 days for small changes), low change failure rate (<15%), fast MTTR (<4 hours).
Code quality gates: No critical/severe static analysis issues; maintainability index ≥ threshold.AI Efficiency- Capture AI‑assisted coding metrics (e.g., % of suggestions accepted, time saved).
- Demonstrate reduction in cycle time and defect rate vs. historical baselines.
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