Job Overview

Accenture is hiring a Custom Software Engineer for a forward-deployed engineering role based in Hyderabad. This full-time opportunity is aimed at experienced software professionals with 5 to 10 years of experience, with the detailed requirements emphasizing hands-on development of AI, machine learning, and large language model based systems.

The role focuses on turning valuable business opportunities into production-ready AI solutions. You will work on retrieval-augmented generation pipelines, agentic workflows, multi-model orchestration, backend services, APIs, enterprise integrations, cloud-native applications, and AI observability. The position combines software engineering fundamentals with modern AI engineering practices and requires professionals who can connect technical solutions with real business outcomes.

You will collaborate with product, architecture, platform, SRE, engineering, and customer-facing teams. The work involves translating business requirements into scalable solutions while following established architectural patterns and engineering standards for security, reliability, performance, compliance, and maintainability.

Key Responsibilities

• Design and develop RAG pipelines that connect language models with relevant enterprise information and business data.
• Build agentic workflows that support complex, multi-step tasks and improve AI-assisted reasoning and tool use.
• Develop multi-model orchestration solutions for domain-specific business applications.
• Build backend services, APIs, and integration layers that connect AI capabilities with enterprise systems and data.
• Work with US-based forward-deployed engineers and other stakeholders to convert prioritized business opportunities into production-ready solutions.
• Implement AI systems within defined architectural patterns and reference designs.
• Contribute to automated build, testing, and deployment workflows through CI/CD pipelines.
• Add structured logging, instrumentation, monitoring, and tracing to AI-enabled applications.
• Help teams measure AI system performance, operating cost, output quality, and reliability.
• Use AI-assisted development tools to improve software quality, debugging, development speed, and engineering productivity.
• Provide design input at the component and feature level while evaluating technical trade-offs.
• Translate business requirements into scalable and maintainable technical implementations.
• Apply engineering standards related to application security, performance, reliability, and compliance.
• Support solutions as requirements evolve and adapt implementation decisions when business priorities change.
• Collaborate with engineering, architecture, product, platform, and SRE teams throughout the software lifecycle.

Required Skills

Strong professional software development experience is required, with the role specifically seeking hands-on experience building AI, ML, or LLM-based systems. Candidates should have practical knowledge of RAG, agentic workflows, or multi-model orchestration and understand how these capabilities can be integrated into enterprise applications.

Backend engineering experience is important. Candidates should be capable of developing REST APIs, services, and integration layers using modern programming languages and frameworks. Python is the mandatory skill identified in the job posting.

The role also requires experience with cloud platforms such as AWS, Azure, or GCP and an understanding of cloud-native application development. Candidates should know how AI services, enterprise data, and application components can be connected securely and reliably.

Experience with agent and tool-use frameworks is required, including connector patterns such as MCP. Knowledge of retrieval, grounding, authentication, authorization, and secure secrets handling is also relevant to building enterprise-grade AI systems.

Preferred Skills

Experience designing high-throughput and low-latency distributed systems is an advantage. Candidates with knowledge of prompt engineering, LLM evaluation, AI output assessment, hallucination detection, or agent tracing may also be well suited to the role.

Familiarity with React or similar frontend technologies can be useful when developing end-user AI experiences. Experience applying AI to log analysis, anomaly detection, or operational insights is another advantage.

Background in forward-deployed engineering, consulting, or customer-facing technical delivery can help candidates succeed because the role requires connecting engineering work with business value across different domains.

Education

The job description specifies 15 years of full-time education as the required educational qualification.

Experience

The job posting states a minimum requirement of 5 years of experience and identifies an overall experience range of 5 to 10 years. The detailed requirements emphasize 7+ years of professional software development experience, including hands-on work with AI, ML, or LLM-based systems. Candidates should therefore carefully assess their experience against both the listed role range and the detailed technical requirements.

Required Technologies

• Python
• Large Language Models (LLMs)
• RAG
• Agentic AI workflows
• Multi-model orchestration
• REST APIs
• Backend services
• AWS
• Microsoft Azure
• Google Cloud Platform (GCP)
• Cloud-native development
• MCP
• CI/CD
• GitLab
• Structured logging
• Distributed tracing
• AI observability
• Prompt engineering
• LLM evaluation frameworks
• React
• Authentication
• Authorization
• Secrets management
• Enterprise integrations

Soft Skills

The position requires strong ownership, clear technical communication, and the ability to work effectively when requirements are changing. Candidates should be comfortable collaborating with product, architecture, engineering, platform, SRE, and business stakeholders.

A business-focused mindset is also important. The engineer should be able to understand the value of a proposed solution, communicate technical trade-offs, and prioritize engineering effort according to business impact. Customer-facing or consulting experience can be especially useful for working across multiple business domains.

Benefits of Working in this Role

This role offers an opportunity to work on modern AI engineering problems that connect LLMs, enterprise data, cloud platforms, software services, and business applications. Professionals can deepen their experience with production AI systems while gaining exposure to agentic architectures, RAG, observability, cloud-native development, and enterprise integration.

The position can also provide valuable experience in translating business needs into production software and collaborating with geographically distributed engineering and customer-facing teams.

Work Mode

The provided job description does not explicitly specify onsite, hybrid, or remote work. The position is based in Hyderabad, Telangana, India. Candidates should confirm the current work arrangement with Accenture during the application process.

Location

This Custom Software Engineer opportunity is based in Hyderabad, Telangana, India. The role supports forward-deployed engineering activities and involves collaboration with product, architecture, engineering, platform, SRE, and business stakeholders.

Who Should Apply

This opportunity is suitable for experienced software engineers who have hands-on experience building AI systems using LLMs and modern cloud technologies. Candidates should have practical experience with RAG, agentic workflows, multi-model orchestration, backend services, REST APIs, enterprise integrations, and cloud-native development.

Professionals with strong Python skills and experience using AWS, Azure, or GCP should consider the role. Candidates who can combine technical engineering ability with business understanding, clear communication, ownership, and customer-focused delivery will be particularly relevant.

Freshers are not the target audience because the position requires substantial professional software development experience.

Career Growth

Working on production AI systems can help experienced engineers develop deeper expertise in LLM application development, AI architecture, cloud engineering, distributed systems, observability, and enterprise software integration. Continued experience with agentic systems, AI evaluation, secure AI development, and business-focused solution design can support future progression into senior engineering, AI architecture, technical leadership, consulting, or specialized AI engineering roles.

Application Advice

Before applying, make sure your resume clearly demonstrates hands-on work with LLM-based applications rather than only theoretical AI knowledge. Highlight specific projects involving RAG pipelines, agentic workflows, multi-model orchestration, backend APIs, enterprise integrations, cloud platforms, and CI/CD.

Mention practical experience with Python, AWS, Azure, or GCP, along with any exposure to MCP or other agent and tool-use patterns. If you have worked on AI observability, prompt engineering, LLM evaluation, hallucination detection, distributed systems, or customer-facing engineering projects, include those details.

Use project examples that explain the problem, your technical contribution, and the business outcome. This will help demonstrate that you can build reliable AI solutions and connect engineering decisions to real-world business requirements.