Full Stack AI Developer
Builds and deploys production-grade agentic AI systems (Python/React) for enterprise clients, focusing on agent workflows, RAG pipelines, and MCP integrations while maintaining DevOps/AgentOps practices.
- Compensation
- Not disclosed
- Location
- Singapore, Singapore
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Fullstack
- Posted
Skills
Must have
- 3+ years of full stack software engineering in production (Python backend + React/Angular/Node.js frontend)
- 3+ years building and deploying LLM-based applications (prompt engineering, RAG, tool integration, agent development)
- Hands-on experience with agentic AI frameworks (agents, tools, orchestration) in production
- 3+ years in classical AI/ML, data engineering, or analytics (model/data pipeline integration)
- 2+ years cloud-native development (AWS/Azure/GCP, containerized workloads, IaC)
- Experience with CI/CD, TDD, version control, and automated testing
- Bachelor’s in Computer Science/Engineering (Master’s preferred)
Nice to have
- Experience with LangGraph, AutoGen, CrewAI, or equivalent agent frameworks
- MCP server/tool integration experience for LLM applications
- RAG pipeline implementation (ingestion, retrieval, re-ranking, quality evaluation)
- Text-to-SQL, knowledge graphs, or Elasticsearch for enterprise knowledge retrieval
- DevOps/AgentOps/LLMOps tooling and CI/CD for AI systems
- Classical AI/ML model integration (prediction APIs, model serving, feature pipelines)
- Active engagement with agentic AI community (open-source, side projects, or published learning)
- Proficiency with AI-assisted development tools (e.g., Claude Code, GitHub Copilot)
Full description
About Accenture Data & AI
The beginning of a new Data & AI decade that will reshape work and society is underway. Accenture is stepping boldly into this future with a clear strategy and purpose: to help clients optimize and reinvent their businesses with data and AI — backed by a $3 billion investment and a commitment to industry-defining work.
About Accenture Data & AI
The beginning of a new Data & AI decade that will reshape work and society is underway. Accenture is stepping boldly into this future with a clear strategy and purpose: to help clients optimize and reinvent their businesses with data and AI — backed by a $3 billion investment and a commitment to industry-defining work.
With over 45,000 professionals dedicated to Data & AI, Accenture's Data & AI organization brings together Experienced Innovation, Strategic Investment, Exceptional Talent, and a Power Ecosystem to deliver outcomes at the frontier of what is possible.
About The Role
The Full Stack AI Consultant (Developer) is a hands‑on engineer at the core of Accenture’s agentic AI delivery capability. This role is for those who build things — full stack applications, agentic workflows, knowledge pipelines, and tool integrations — and who want to do that work at the frontier of enterprise AI. Consultants work within delivery teams, turning business requirements into production‑grade agentic AI solutions under the guidance of technical leads and managers.
The expectation is active, daily engineering: writing code, building agents, implementing MCP servers, designing knowledge pipelines, and maintaining the DevOps and AgentOps practices that keep production systems running. Consultants on this team are also expected to invest continuously in learning — agentic AI is evolving rapidly, and staying current is a professional responsibility, not optional.
Position Responsibilities
Full Stack Application Development
- Design and build full stack agentic AI applications — Python backends, REST and event‑driven APIs, and React or equivalent frontends — to production engineering standards.
- Implement agentic application UX: streaming responses, intermediate output display, reasoning transparency, and error and escalation interfaces for end users.
- Translate business requirements into technical specifications; work with managers and clients to clarify scope, surface ambiguities, and deliver against agreed outcomes.
Agentic AI Development
- Build and configure agents using established orchestration frameworks (LangGraph, AutoGen, or equivalent): harness setup, persona and instruction loading, tool binding, memory configuration, and lifecycle management.
- Implement reasoning patterns (ReAct, Chain‑of‑Thought, Plan‑and‑Execute) appropriate to each agent use case; design and version prompt architecture including system prompts, few‑shot examples, and structured output schemas.