- Company Name
- Xcede
- Job Title
- Senior AI Engineer
- Job Description
-
Job title: Senior AI Engineer
Role Summary:
Build and maintain end‑to‑end AI systems that ingest diverse data formats, apply retrieval‑augmented LLM techniques, and deliver production‑grade applications. Integrate solutions into Azure cloud, ensure reliability, monitoring, and responsible AI practices while collaborating cross‑functionally with engineering, product, and data teams.
Expactations:
- 2+ years delivering production AI/LLM solutions in a data‑centric environment.
- Advanced Python development and proficiency with machine‑learning libraries (PyTorch, TensorFlow, scikit‑learn).
- Experience with vector databases (pgvector, Pinecone, Azure Search) and graph databases (Neo4j).
- Proven ability to scale AI pipelines for real‑world workloads and maintain high reliability.
- Strong communication skills to articulate technical concepts to varied stakeholders.
Key Responsibilities:
- Design and implement data ingestion pipelines for text, spreadsheets, JSON, and audio.
- Develop intelligent retrieval systems combining chunking, indexing, re‑ranking, and hybrid search.
- Build LLM‑based applications for extraction, classification, summarisation, and semantic querying.
- Construct multi‑step LLM workflows using frameworks such as LangChain or LangGraph, integrating tool usage and agent coordination.
- Define and enforce testing/validation procedures for model accuracy, factuality, and relevance.
- Embed AI capabilities into user‑facing platforms in collaboration with product and engineering teams.
- Deploy solutions to Azure, leveraging container orchestration, CI/CD pipelines, secure key management, and observability tooling.
- Implement data security and responsible AI measures, including anonymisation, access control, and auditability.
Required Skills:
- Python programming, machine‑learning libraries, LLM integration.
- Vector database and search tooling; graph database experience (Neo4j).
- Retrieval‑augmented generation and structured output design.
- Cloud deployment (Azure) and container orchestration (Kubernetes / Docker).
- Version control, CI/CD, and infrastructure automation.
- Front‑end integration knowledge (Angular) is a plus.
- Familiarity with content‑heavy domains (publishing, research, legal) is advantageous.
Required Education & Certifications:
- Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related field.
- Relevant certifications (e.g., Azure AI Engineer Associate, TensorFlow Professional) are welcome but not mandatory.