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PHOENIQS

Delivers managed analytics platforms and consulting

AI Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeBasel, SwitzerlandNo salary listedIn-person

About the Job

Requirements

  • At least 3 years of experience in software engineering or applied AI.
  • Strong Python skills.
  • Hands-on experience with agent frameworks such as LangChain, DSPy, AutoGen, or CrewAI, or equivalent frameworks.
  • Strong understanding of agent design patterns, including planner/executor, memory and reflection loops, tool selection, and self-evaluation.
  • Expertise in prompt engineering, including Chain-of-Thought, ReAct, structured prompting, and output validation.
  • Experience building retrieval-augmented generation (RAG) systems, including chunking strategies, embedding optimization, and retrieval tuning.
  • Experience with vector databases such as FAISS, Pinecone, Weaviate, or OpenSearch.
  • Experience deploying large language models (LLMs) via APIs or open-source models such as LLaMA, Mistral, Qwen, or DeepSeek.
  • Familiarity with inference frameworks such as vLLM, TGI, or Ollama.
  • Experience integrating tools such as REST APIs, databases, file systems, and browser automation.
  • Working knowledge of Docker, Git, and AWS, GCP, or Azure.

Responsibilities

  • Architect and develop agentic AI systems using frameworks such as LangChain, AutoGen, DSPy, CrewAI, or custom orchestration layers.
  • Design modular, goal-oriented workflows for autonomous task planning and inter-agent collaboration.
  • Implement planner–executor loops, ReAct, reflection, and self-verification patterns.
  • Engineer prompting strategies including Chain-of-Thought, Tree-of-Thought, structured outputs, and prompt versioning.
  • Build persistent short-term, long-term, and episodic memory systems.
  • Integrate LLMs with execution environments including Python, Bash, SQL, REST APIs, and browser automation.
  • Design and optimize RAG pipelines using vector databases and hybrid search.
  • Implement API orchestration, tool calling, and code-execution agents.
  • Deploy scalable AI systems using Docker, CI/CD, and cloud platforms.
  • Monitor agent behavior, execution traces, and reasoning paths.
  • Implement human-in-the-loop workflows to support safe autonomy.
  • Collaborate with Product, Engineering, Data Science, and UX teams to deliver AI solutions.

Desired Qualifications

  • Production experience building AI copilots, taskbots, or autonomous agents.
  • Experience with observability and safety tools such as Guardrails AI, Rebuff, or HoneyHive.
  • Experience with model routing and dynamic model selection.
  • Experience optimizing context windows.
  • Experience with fine-tuning techniques such as LoRA or QLoRA.
  • Contributions to open-source agent or LLM tooling.
  • Familiarity with prompt-injection mitigation, alignment strategies, and AI safety best practices.

About the Company

PHOENIQS

Delivers managed analytics platforms and consulting

PHOENIQS provides managed technology services and analytics consulting, designing and building custom analytics platforms for clients in financial services, media, and eCommerce. It follows a three-step process—design, build, and knowledge transfer—with an option to hand over a ready-to-run platform after client teams are prepared, and it earns consulting fees plus ongoing managed services. The company also hosts and scales platforms, migrating client data centers to secure off-site facilities and addressing data discipline gaps essential for deploying machine learning and AI. Its goal is to help clients become data-driven by delivering scalable, secure analytics environments and sustainable data platforms with end-to-end delivery and long-term support.

Company size
1-10
Company stage
Not listed
Total funding
Not listed
Headquarters
Philadelphia, PA
Founded
2020

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