AI Engineer

The AI Engineer is a hands‑on technical expert responsible for prototyping, integrating, and refining AI-powered solutions that deliver immediate business value. Collaborating closely with business stakeholders, data teams, and data science experts, this role transforms innovative ideas into scalable, production‑ready AI systems—ranging from internal copilots and workflow automation tools to retrieval‑augmented assistants and analytics platforms. The ideal candidate is passionate about large language models, rapid data integration, and iterative development in a fast-paced environment. Key Responsibilities Solution Prototyping & Integration Develop AI-driven prototypes and pilots, including chatbots, intelligent agents, workflow automations, and retrieval‑augmented question‑answering systems for enterprise use. Integrate large language models (LLMs) and AI services with enterprise infrastructure—such as APIs, data warehouses, productivity platforms, and document repositories. Design and implement vector retrieval and embedding pipelines; establish lightweight orchestration layers for pilot workflows (e.g., routing, context injection, guardrails). Lead prompt engineering efforts, system prompt design, and maintain prompt libraries for reuse across teams. Conduct small‑scale assessments covering quality, response latency, hallucination rates, operational costs, and user satisfaction metrics. Instrument prototypes to gather feedback and usage data, enabling continuous iterative improvements. Collaborate with data and platform teams to transition successful pilots into scalable, governed, and supportable solutions. AI Safety, Policy & Guardrails Implement privacy, security, and compliance controls such as role‑based access, content moderation, and data redaction within AI solutions. Embed governance guardrails into pilots through model routing logic, input/output filters, and safety triggers to ensure secure and responsible deployment. Internal Enablement Document technical architecture, data flows, prompt configurations, limitations, risks, and operational guidelines for each AI solution. Partner with adoption teams to train business users on AI workflows, enabling seamless integration into daily operations. Core Qualifications & Requirements Minimum of 4 years of hands‑on experience in software engineering, machine learning engineering, data science, or related technical fields. Proven experience working with large language models (LLMs), including commercial APIs, open‑source models, RAG pipelines, vector databases, and orchestration frameworks. Practical knowledge of modern data platforms such as cloud data warehouses (e.g., Snowflake), data transformation tools (e.g., dbt), and cloud services. Ability to work effectively in ambiguous problem spaces by collaborating with stakeholders to map manual workflows and rapidly develop AI‑enabled solutions. Deep understanding of AI governance, security, privacy policies, and responsible AI practices within enterprise settings. Excellent written and verbal communication skills, with an ability to explain technical concepts clearly to non‑technical audiences. Bachelor’s degree in Computer Science, Data Science, Engineering, Operations Research, or a related field; graduate studies preferred. #J-18808-Ljbffr The Phoenix Group