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-based assistance for enterprise use cases
Strong understanding of AI framework ecosystem for RAG implementation, agent orchestration for SDLC use cases, and AI-enabled business process automation patterns

Professional & Technical Skills:
End-to-end full stack enterprise engineering
Hands-on architecture and system design leadership
Demonstrated 100 percent hands-on contribution model in senior engineering roles
Strong Java-centric backend engineering depth and modern frontend engineering depth
Practical Python capability for custom cloud agents and enterprise integrations
Strong AWS engineering understanding for design and operations
AI-native SDLC adoption with secure guardrails
Enterprise reliability, security, compliance, and data engineering mindset

Additional Information:
- The candidate should have minimum 5 years of experience in Kubernetes.
- This position is based at our Gurugram office.
- A 15 years full time education is required.

15 years full time education

About Accenture

Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.

Visit us at www.accenture.com 

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