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Accenture

Posted 31 Aug 2026

Full Stack & AI - GenAI Engineer (Python)

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Employment
Full time
Team
Data Science & Analytics
Posted
31 Aug 2026

About the role

Seeking an experienced Python Full-Stack and AI/GenAI Engineer with expertise in Python, React.js, LLMs, RAG, agentic AI, and cloud-native application development. The ideal candidate should have hands-on experience in building scalable full-stack applications, AI-powered solutions, data pipelines, and enterprise-grade GenAI platforms.


Responsibilities:

  • 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.
  • Design, develop, and lead Python-based full-stack and GenAI applications.
  • Build scalable REST APIs and responsive React.js front-end applications.
  • Develop and optimize RAG pipelines, vector search, and agentic AI workflows.
  • Design secure authentication and authorization mechanisms.
  • Build data ingestion and processing pipelines for structured/unstructured data.
  • Mentor team members and contribute to architecture and technical decisions.


Requirements:

  • Minimum 3+ years of Python development experience.
  • Hands-on experience in React.js and AI/GenAI solution development.
  • Strong problem-solving and scenario-based troubleshooting skills.
  • 15 years of full-time education.


Must-Have Skills:

  • Python, OOP, Design Patterns, Unit Testing.
  • Flask, FastAPI, Django REST Framework.
  • Authentication and Authorization (JWT, OAuth, SSO).
  • Data Ingestion, Data Pipelines, Large File Handling.
  • NumPy, Pandas, DataFrames, Prefect.
  • Performance Tuning and Code Optimization.
  • SQL, PL/SQL, Data Modeling, Shell Scripting
  • React.js, Redux, React Hooks, Context API.
  • REST API Integration (Axios, Fetch).
  • Jest, React Testing Library
  • Git, CI-CD.
  • AI / GenAI Skills
  • LLM Integration, GenAI Tooling
  • RAG Pipeline Development and Optimization.
  • LangChain, LangGraph, and Sentence Transformers.
  • Hugging Face Transformers
  • Vector Databases (FAISS, Qdrant, Weaviate).
  • Embedding Models (OpenAI, BERT, or Similar).
  • Agentic AI Workflows.
  • Semantic Search and Vector Search.
  • Grafana Monitoring and Observability.


Good to Have:

  • Docker, Kubernetes, Containerization.
  • AWS-Azure Cloud
  • Microservices Architecture.
  • Azure OpenAI, Bedrock, LlamaIndex, MCP

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