RRemoteville

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Mid-Senior AI Engineer

Skills
Agentic AI DevelopmentMlopsRetrieval-Augmented GenerationPythonLangGraphLangChainLlamaIndex
Role

What the job involves

The main requirements, responsibilities and hiring steps.

Requirements

  • Bachelor’s degree in Computer Science Artificial Intelligence Engineering or equivalent practical experience
  • 4+ years experience building and deploying production AI applications
  • 2+ years experience developing LLM-powered products conversational AI RAG systems or agentic workflows
  • Strong Python skills and experience building backend systems and APIs
  • Hands on experience building agentic workflows tool-calling pipelines and stateful orchestration using frameworks such as LangGraph LangChain LlamaIndex AutoGen CrewAI or similar
  • Strong experience designing and optimising retrieval systems including vector stores graph-based RAG architectures hybrid retrieval reranking and semantic search pipelines
  • Experience with production optimisation for RAG systems including latency optimisation token cost control context management monitoring guardrails and prompt versioning
  • Exposure to AIOps/MLOps practices and hands-on experience with AWS Bedrock SageMaker Vertex AI or similar cloud AI platforms
  • Comfortable working autonomously and taking ownership in a fast-moving remote startup environment

Nice to have

  • Master’s degree
  • Evaluation frameworks
  • Graph databases
  • Recommendation systems
  • Modern web apps
  • Docker
  • CI/CD

Day to day

  • Design, build, and deploy agentic AI workflows and RAG-powered applications for real-world use cases
  • Develop scalable backend services and APIs powering AI agents and conversational systems
  • Build and optimise retrieval pipelines including embeddings, chunking, reranking, memory and vector search
  • Collaborate closely with product, design, and engineering teams to shape AI-first experiences
  • Integrate LLM systems into user-facing web applications and product features
  • Improve response quality, reliability, latency, observability and cost efficiency of production AI systems
  • Evaluate new AI frameworks, tools, and orchestration approaches across the rapidly evolving LLM ecosystem
  • Influence architecture decisions for scalable, secure, and production-ready AI infrastructure