Senior Engineer (GenAI & Prompt Engineering)
About Xebia
Xebia is a trusted advisor in the modern era of digital transformation, serving hundreds of leading brands worldwide with end-to-end IT solutions. The company has experts specializing in technology consulting, software engineering, AI, digital products and platforms, data, cloud, intelligent automation, agile transformation, and industry digitization. In addition to providing high-quality digital consulting and state-of-the-art software development, Xebia has a host of standardized solutions that substantially reduce the time-to-market for businesses.
Xebia also offers a diverse portfolio of training courses to help support forward-thinking organizations as they look to upskill and educate their workforce to capitalize on the latest digital capabilities. The company has a strong presence across 16 countries with development centres across the US, Latin America, Western Europe, Poland, the Nordics, the Middle East, and Asia Pacific.
Job Title: Contractor – Senior Engineer (GenAI & Prompt Engineering)
About the Role
We are looking for a highly experienced GenAI Engineer with deep expertise in prompt engineering, retrieval-augmented generation (RAG), and vector-based search systems to help integrate GenAI into our engineering and DevOps ecosystem.
As a contractor in this role, you will build intelligent, context-aware assistants and agents that augment CI/CD workflows, knowledge retrieval, incident handling, and developer productivity using LLMs, Python, and NLP pipelines. You will lead the charge in making our platform smarter and more autonomous by embedding GenAI natively into infrastructure and developer experience workflows.
Key Responsibilities
• Design, iterate, and optimize prompts for task-specific LLM workflows with high accuracy, relevance, and controllability
• Build RAG pipelines using vector databases like FAISS, Weaviate, Pinecone, or similar to support knowledge retrieval and contextual AI agents
• Integrate LLMs into internal engineering tools, CI/CD workflows, and observability systems for real-time summarization, automation, and productivity enhancement
• Leverage Python-based frameworks like LangChain, LlamaIndex, Haystack,and others to implement end-to-end GenAI solutions
• Fine-tune open-source or commercial models (OpenAI, Claude, Cohere, Mistral,etc.) as needed for domain-specific use cases
• Collaborate with DevOps and platform teams to identify automation and GenAI augmentation opportunities
• Ensure data privacy, governance, and ethical standards are met when using internal datasets or embedding knowledge
• Optimize response quality through prompt chaining, feedback loops, and context compression techniques
• Experience integrating GenAI into CI/CD tools, observability dashboards, or chat-based interfaces
Required Skills & Experience
• 6–10+ years in engineering roles with 2+ years hands-on in GenAI, NLP, or LLMbased systems
• Proven experience in prompt engineering and LLM integration for backend or infrastructure automation
• Strong Python development skills and working knowledge of LangChain, LlamaIndex, Hugging Face Transformers, or similar GenAI libraries
• Deep understanding of vector databases (e.g., FAISS, Pinecone, Weaviate, Qdrant) and embedding techniques
• Experience designing and deploying RAG architectures for internal knowledge access and contextually intelligent systems
• Familiarity with NLP concepts like tokenization, summarization, intent extraction, semantic similarity, etc.
• Experience with LLM APIs (OpenAI, Azure OpenAI, Claude, etc.) and streaming response generation
• Ability to work in fast-paced, iterative development cycles in a collaborative DevOps/platform engineering environment
Nice to Have
• Understanding of multi-modal models or fine-tuning open LLMs for specialized use
• Familiarity with developer-facing GenAI use cases: changelog generation, log triage, infra as code review, chatbot copilots
• Experience working with Kubernetes, GitOps, or DevSecOps platforms a plus
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