Job Overview
Role: Application Support Engineer (AI & GenAI Focus) Location: Mumbai Experience: 0-3 Years Qualification: B.E / B.Tech / M.Tech (CS, AI, Data Science) Key Skills: Python, GenAI, LangChain, RAG, Java Full Stack
Job Description
Accenture is officially hiring for the Application Support Engineer role. This is a unique opportunity for B.E/B.Tech graduates with 0-3 years of experience to work at the intersection of traditional support and modern Generative AI development. Based in Mumbai , you will identify and solve critical issues while building innovative RAG pipelines and Agentic AI workflows. As an Application Support Engineer at Accenture under the Accenture Recruitment 2026 drive, you will act as a software detective, identifying and solving issues within complex business systems. However, this isn't a traditional support role—you will be deeply involved in Full Stack AI development . You will design and build RAG-based applications, manage LLM inferencing pipelines, and develop scalable microservices using NestJS and Python. The ideal candidate possesses an experimental mindset, capable of rapidly prototyping solutions in unknown problem spaces. Whether you are troubleshooting a critical system component or experimenting with emerging protocols like the Model Context Protocol (MCP), your work will ensure that Accenture's AI-driven systems remain production-grade and defect-free.
Roles and Responsibilities
- GenAI Application Development: Design and develop Generative AI applications using frameworks like LangChain and LangGraph .
- RAG Pipeline Management: Build and maintain RAG (Retrieval Augmented Generation) pipelines for enterprise knowledge systems.
- Troubleshooting: Identify, troubleshoot, and solve technical issues within critical business components as a "software detective."
- Microservices Development: Develop scalable microservices using NestJS and Python with secure REST API practices.
- LLM Inferencing: Implement LLM inferencing pipelines and vector search pipelines for document ingestion.
- AI Experimentation: Experiment with emerging AI standards and multi-agent orchestration patterns.
- Architecture Collaboration: Collaborate with cross-functional teams to translate functional requirements into technical architecture.
- Documentation & MLOps: Maintain clear documentation and contribute to MLOps/LLMOps practices.
Skills and Eligibility Criteria
Educational Background: B.E / B.Tech / M.Tech in Computer Science, AI, Data Science, or related fields (15 years full-time education).
Experience: 0–3 years overall; specifically seeking candidates with exposure to Java Full Stack Development and Python.
Mandatory Technical Skills:
- LLMs
- Prompt engineering
- Semantic search
- Embedding models (Hands-on experience with AI Core)
- RAG (Implementation experience)
- Agentic AI (LangChain/LangGraph - Implementation experience)
- Vector search (Implementation experience)
- Python (Strong proficiency)
- Java Full Stack Development (Exposure)
Competencies:
- NestJS (Knowledge is a plus)
- DevOps (Knowledge is a plus)
- Strong analytical skills
- Innovation mindset
- Ability to learn new frameworks rapidly