Fullstack AI

Role & responsibilities

Enterprise AI Solutions

Experience: 7+ years overall software development | Focus: Implementation, integration &

productionization

Job Summary

We are seeking an experienced AI Application Developer to build and deliver enterprise-grade AI

solutions. The ideal candidate combines strong software engineering fundamentals with hands-on

experience developing production-ready applications using Large Language Models (LLMs),

Retrieval-Augmented Generation (RAG), AI agents, and modern AI integration frameworks.

This role centers on implementation, integration, optimization, and productionization of AI-powered

solutions rather than AI research or model training.

Responsibilities

• Design and develop scalable backend services and REST/GraphQL APIs
• Implement AI-powered features using LLMs and generative AI technologies
• Build, maintain, and optimize RAG pipelines, including vector database integration and retrieval

strategies

• Develop AI agents and tool integrations, including MCP-based workflows
• Apply prompt engineering and prompt management practices across AI features
• Optimize AI application performance, latency, and token/cost consumption
• Implement AI security controls, guardrails, and governance requirements
• Monitor, troubleshoot, and continuously improve production AI systems
• Collaborate with architects and product teams to deliver enterprise AI solutions

Required Skills

Software Engineering

• Python
• Microservices architecture
• REST & GraphQL APIs
• Authentication & authorization
• Caching strategies
• Cloud-native development

AI Engineering

• LLM APIs (OpenAI, Anthropic, Gemini, Azure OpenAI)
• RAG architectures & vector databases
• Agent frameworks and MCP concepts & integrations
• Prompt engineering & prompt management
• Context engineering and AI workflow orchestration

Production Readiness

• Cost and latency optimization
• AI observability
• Security and compliance
• Production deployment experience

Nice to Have

Experience with tooling such as LiteLLM (LLM gateway/routing) or Langfuse (prompt & observability tracing)

is a plus