Senior AI Full Stack Developer (Python & React)

About us:

Accenture Industry X, part of Accenture, helps organizations thrive in the digital era by combining deep industry expertise with data, AI, cloud, and digital engineering capabilities.

We partner with leading organizations across industries to design and deliver intelligent, scalable solutions that create measurable business value and transform the way companies operate.

About the team:

At Accenture, we are at the forefront of the AI evolution, working with global clients to transform their businesses into AI-enabled enterprises.

Our teams design and build next-generationGenerative AI and Agentic AI solutions, ranging from intelligent applications and AI agents to enterprise-grade AI platforms, RAG architectures, and observability capabilities.

We work in fast-moving, cross-functional teams where software engineering, AI, cloud, architecture, design, and business strategy come together.

We are looking for an experiencedSenior AI Full Stack Developer with strong expertise inPython and React/TypeScript who can design, build, and deploy production-grade AI applications from end to end.

Your Role:

As a Senior AI Full Stack Developer, you will be responsible for developing modern AI-powered applications across the complete technology stack — from Python backend services and AI orchestration layers to responsive React-based user experiences.

You will work hands-on withLarge Language Models (LLMs), Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), APIs, cloud platforms, and modern DevOps practices, turning AI concepts and prototypes into secure, scalable, observable, production-ready solutions.

Key Responsibilities

• Design, develop, and maintainend-to-end AI-powered applications using Python and React/TypeScript.
• Build scalable backend services andREST APIs using Python frameworks such asFastAPI, Flask, or Django.
• Develop modern, responsive, and reusable front-end applications usingReact, TypeScript, Next.js, HTML, and CSS.
• Integrate front-end applications with backend services, AI services, enterprise APIs, and data platforms.
• Design and implementGenerative AI and Agentic AI applications powered by Large Language Models.
• Build AI workflows and agent-based systems using frameworks such asLangChain, LangGraph, LlamaIndex, Haystack, or similar technologies.
• Design and implementRetrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking, embedding generation, retrieval, reranking, and response generation.
• Integrate vector databases and search technologies such asPinecone, Weaviate, FAISS, Milvus, Azure AI Search, Elasticsearch, or equivalent solutions.
• Integrate commercial and open-source LLMs through platforms and APIs such asAzure OpenAI, OpenAI, AWS Bedrock, Google Vertex AI, Hugging Face, or similar services.
• Applyprompt engineering, structured outputs, tool/function calling, context management, and model evaluation techniques to improve AI application quality and reliability.
• Implement AI agent capabilities includingtool usage, workflow orchestration, memory/state management, multi-agent patterns, and human-in-the-loop workflows where appropriate.
• Implement appropriateLLM evaluation, tracing, monitoring, guardrails, and observability mechanisms for production AI systems.
• Optimize AI applications foraccuracy, latency, scalability, reliability, and cost.
• Design secure application architectures covering authentication, authorization, API security, secrets management, and secure handling of enterprise data.
• Deploy and operate applications inAWS, Microsoft Azure, or Google Cloud Platform (GCP).
• Containerize applications usingDocker and work with orchestration platforms such asKubernetes.
• Implement infrastructure and deployment automation usingInfrastructure as Code (IaC) technologies such as Terraform.
• Build and maintainCI/CD pipelines and apply DevOps, MLOps, LLMOps, and AIOps practices.
• Implement application and AI observability through logging, metrics, tracing, monitoring, and alerting.
• Write clean, maintainable, reusable, well-documented, and testable code across the full technology stack.
• Implement unit, integration, API, and end-to-end testing to ensure application quality.
• Participate in architecture and technical design discussions and contribute to technology and engineering decisions.
• Perform code reviews, establish engineering best practices, and support other developers through technical guidance and knowledge sharing.
• Collaborate closely with AI engineers, software engineers, architects, data engineers, DevOps engineers, designers, product owners, and client stakeholders.
• Evaluate emerging AI technologies and determine how they can be applied effectively in enterprise environments.
• Take ownership of solutions fromconcept and prototyping through production deployment, monitoring, and continuous improvement.

What you need to succeed:

We are looking for an experienced software engineer who combines strongfull-stack engineering fundamentals with hands-on experience building modern AI applications.

Core Technical Skills

• Strong professional experience withPython and modern Python software development.
• Strong professional experience withReact and TypeScript.
• Experience developing production-grade backend services usingFastAPI, Flask, Django, or similar frameworks.
• Strong understanding ofREST APIs, asynchronous programming, microservices, distributed systems, and API integration.
• Experience building modern front-end architectures, reusable components, state management, API integration, and responsive user interfaces.
• Strong understanding of software engineering principles, includingclean code, design patterns, testing, version control, and code review practices.
• Experience working with relational and/or NoSQL databases such asPostgreSQL, MySQL, MongoDB, Redis, or equivalent technologies.

Generative AI & Agentic AI

• Hands-on experience developing applications powered byLarge Language Models (LLMs).
• Experience withGenerative AI, Agentic AI, prompt engineering, tool/function calling, and structured LLM outputs.
• Experience with AI orchestration frameworks such asLangChain, LangGraph, LlamaIndex, Haystack, or similar technologies.
• Practical experience designing and implementingRAG architectures.
• Experience withembeddings, semantic/vector search, retrieval strategies, reranking, and vector databases.
• Understanding of common LLM challenges includinghallucinations, context limitations, prompt injection, latency, cost, security, and evaluation.
• Experience implementingLLM observability, evaluation, tracing, and guardrails.
• Knowledge of fine-tuning approaches such asLoRA and PEFT is an advantage.

Cloud & DevOps

• Hands-on experience with at least one major cloud platform:Microsoft Azure, AWS, or GCP.
• Experience withDocker and containerized application development.
• Experience withKubernetes or equivalent container orchestration technologies.
• Experience implementingCI/CD pipelines.
• Familiarity withInfrastructure as Code, preferably Terraform.
• Understanding ofDevOps, MLOps, LLMOps, AIOps, monitoring, logging, and distributed tracing.
• Experience designing scalable, resilient, secure, and observable production systems.

Nice to Have

• Experience designingmulti-agent or complex agentic workflows.
• Experience with event-driven architectures and messaging technologies such as Kafka or RabbitMQ.
• Experience with streaming AI responses and technologies such as WebSockets or Server-Sent Events.
• Experience with enterprise authentication and authorization standards such asOAuth 2.0, OpenID Connect, and JWT.
• Experience with AI observability and evaluation platforms.
• Experience working with enterprise search, knowledge management, or document-processing solutions.
• Understanding of responsible AI principles, AI security, privacy, governance, and regulatory considerations.
• Experience working directly with clients and translating business requirements into technical solutions.
• Experience leading technical initiatives, mentoring developers, or contributing to architecture decisions.

What We Value

Beyond technical expertise, we are looking for someone who:

• Has a strongengineering mindset and takes ownership of solutions end to end.
• Is passionate about AI while maintaining a pragmatic approach to building reliable production software.
• Is curious and continuously explores emerging technologies and engineering practices.
• Can independently investigate complex technical problems and propose practical solutions.
• Communicates clearly with both technical and non-technical stakeholders.
• Enjoys collaborating in multidisciplinary and international teams.
• Balances speed and experimentation with maintainability, security, scalability, and engineering quality.
• Is comfortable working in an evolving technology landscape where tools, frameworks, and AI capabilities change rapidly.

Join us for a fulfilling career and a balanced lifestyle that celebrates diversity and promotes inclusivity. 

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About Accenture

Visit us at www.accenture.com 

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