Custom Software Engineer

Project Role : Custom Software Engineer Project Role Description : Develop custom software solutions to design, code, and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs. Must have skills : Kubernetes, Machine Learning (ML), Amazon Web Services (AWS), Java Full Stack Development Good to have skills : NA Minimum 5 year(s) of experience is required Educational Qualification : 15 years full time education Summary: As a Custom Software Engineer, a typical day involves designing and developing tailored software solutions that enhance various system components or applications. The role requires working within dynamic teams to build scalable and efficient software using contemporary development methodologies. Collaboration and continuous improvement are key, as the engineer contributes to evolving software architectures and ensures alignment with business objectives through iterative development cycles and agile practices. Roles & Responsibilities: Full Stack Engineering and Architecture Lead architecture and implementation of enterprise full stack solutions across frontend, backend, integration, and data layers Contribute directly to implementation as a 100 percent hands-on engineer while leading design and architecture decisions Design and build secure, scalable user-facing applications with robust APIs and service contracts Define component and service boundaries with clear ownership, maintainability, and backward compatibility Drive system decomposition and integration patterns for large, regulated enterprise platforms Ensure architecture decisions are translated into production-grade code and delivery plans Frontend and Experience Engineering Build modern, accessible, high-performance web experiences for operations, servicing, and platform workflows Define frontend architecture standards for modularity, testability, and observability Implement secure frontend integration patterns with API gateways and identity systems Improve developer productivity with reusable UI components and platform-aligned design systems Backend and Platform Engineering Build robust domain services, event-driven integrations, and enterprise APIs with strong reliability guarantees Implement resilience patterns including retries, idempotency, circuit breaking, timeout propagation, and graceful degradation Design and optimize persistence models, read models, and data access patterns for scale and auditability Drive platform engineering practices for CI/CD, deployment automation, and quality gates AI-Native Engineering Embed AI-native workflows across SDLC: coding acceleration, automated testing, impact analysis, defect triage, and operational diagnostics Define enterprise guardrails for AI usage including source grounding, secure prompts, policy controls, and human review Improve engineering KPIs using AI-assisted practices while preserving quality and compliance standards Build reusable AI templates and playbooks for full stack teams Drive modernized engineering ways of working that increase delivery velocity without compromising reliability, security, and compliance Demonstrate practical understanding of AI frameworks used to build RAG solutions, agentic SDLC workflows, and AI-enabled business workflows wherever required Security, Compliance, and Reliability Enforce secure-by-design principles across UI, APIs, services, and data layers Build observability-by-default with logs, metrics, traces, and business process monitoring Ensure auditable change trails and reproducibility of critical business workflows Drive SLO/SLI based engineering operations and incident readiness Engineering Leadership Mentor engineers and lead design and code review practices Align delivery teams on architecture principles, coding standards, and non-functional requirements Partner with product, architecture, and operations stakeholders on delivery roadmaps and risk management Own technical outcomes from design through production support Core Skills Strong full stack engineering experience across modern frontend and backend ecosystems Deep backend engineering in Java and related enterprise backend stacks (Spring Boot, API, integration, security, data access) Strong frontend engineering with modern frameworks (React/Angular), accessibility, state management, and performance optimization Strong Python engineering for custom cloud agents, enterprise automation, and service integrations where required Distributed systems and event-driven architecture experience in production environments Strong AWS cloud engineering understanding (compute, storage, networking, IAM, observability, serverless, and integration services) Cloud-native engineering experience (Kubernetes, CI/CD, infrastructure automation, GitOps) Strong observability, reliability, and incident response practices Secure software engineering experience in regulated enterprise environments Proven experience applying AI-native engineering practices in real delivery workflows Strong data engineering fundamentals (data modeling, pipeline reliability, data quality, schema evolution, and performance tuning) Strong engineering practices: clean architecture, secure coding, code review, testing strategy, documentation, and production readiness Architecture and Leadership Skills Ability to design end-to-end enterprise systems from UI to data and operations Strong decision-making on architectural trade-offs and technical debt management Experience influencing standards across multiple teams and delivery streams Strong communication with both business and technical stakeholders AI-Native Skills Practical experience using LLM-enabled engineering workflows for productivity and quality Understanding of AI governance, secure usage, and human-in-the-loop controls Familiarity with grounded AI patterns such as retrieval-