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> Djinni

AI Engineer

pythonapillmragai agentsawsazuregoogle cloudany one ofawsdockerci/cd
langchainlanggraphpineconeweaviateqdrantpgvectorkubernetesmlops
englishB2
experience5+ years
domainAI
> full description

About the Role

We’re looking for an experienced Forward Deployed Engineer (FDE) to build and deploy real-world AI solutions for international clients.

This role combines strong software engineering skills with a practical understanding of business needs.

You’ll work directly with clients, understand their challenges, design technical solutions, and take AI-powered applications from concept to production.

We’re looking for someone who can do more than write code — someone who can take ownership and make things work.

What You’ll Do

  • Work directly with clients to understand their business needs and technical requirements.
  • Design, build, and deploy AI-powered applications and integrations.
  • Develop LLM-based solutions, RAG pipelines, and AI agents.
  • Integrate AI solutions with existing APIs, databases, and enterprise systems.
  • Build scalable backend services and production-ready infrastructure.
  • Own the full implementation cycle, from discovery and prototyping to production deployment.
  • Troubleshoot technical challenges and continuously improve deployed solutions.

Requirements

  • 5+ years of software engineering experience.
  • Strong Python development skills.
  • Experience with backend development, APIs, and system architecture.
  • Hands-on experience with LLMs (OpenAI, Anthropic, or open-source models).
  • Practical knowledge of RAG, AI agents, and LLM integrations.
  • Experience with cloud platforms (AWS, GCP, or Azure).
  • Docker, CI/CD, and production deployment experience.
  • Strong problem-solving skills and ability to work independently.
  • Comfortable communicating directly with international clients.
  • English B2+.

Nice to Have

  • Experience with LangChain, LangGraph, or similar frameworks.
  • Experience with vector databases (Pinecone, Weaviate, Qdrant, pgvector).
  • Kubernetes and distributed systems.
  • Data engineering or ML infrastructure background.
  • Experience as a Solutions Engineer, Solutions Architect, or Technical Consultant.
  • Previous experience in client-facing engineering roles.