Principal AI Engineer

<p><span>We are seeking a <strong>Principal AI Engineer</strong> with <strong>deep, hands‑on experience in Large Language Models (LLMs)</strong> to lead the design, development, and deployment of <strong>enterprise‑grade AI‑powered automation systems</strong> across the organization.</span></p> <p><span>This role goes beyond experimentation. You will <strong>own and deliver production‑scale AI solutions</strong>, analyze complex internal workflows, identify high‑value automation opportunities, and architect intelligent systems that materially improve efficiency, accuracy, and scalability.<br/><br/></span></p> <p><span>This position is ideal for a <strong>seasoned engineer (10+ years)</strong> who combines <strong>strong technical depth, architectural judgment, and a product mindset</strong>, and who enjoys building <strong>practical, high‑impact AI systems used at scale</strong>.</span></p> <h2><span><strong>KEY RESPONSIBILITIES:</strong></span></h2> <ul> <li><span>Lead the <strong>design, development, and deployment</strong> of LLM‑based automation solutions across multiple business functions.</span></li> <li><span>Work closely with cross‑functional teams to <strong>define problem statements, data requirements, system boundaries, and solution approaches</strong>.</span></li> <li><span>Architect and implement <strong>end‑to‑end LLM systems</strong>, including: </span></li> <ul> <li><span>Prompt pipelines</span></li> <li><span>Agent‑based architectures</span></li> <li><span>Retrieval‑Augmented Generation (RAG) systems</span></li> <li><span>Internal AI services and APIs</span></li> </ul> <li><span>Integrate commercial and open‑source LLMs (e.g., OpenAI, Anthropic, Databricks, open‑source models) into <strong>enterprise systems and products</strong>.</span></li> <li><span>Drive <strong>model evaluation, prompt optimization, and system reliability improvements</strong> based on real‑world usage.</span></li> <li><span>Establish and maintain <strong>monitoring, logging, and evaluation frameworks</strong> for LLM‑driven applications.</span></li> <li><span>Partner with product, operations, security, and engineering teams to <strong>map workflows and identify high‑ROI automation opportunities</strong>.</span></li> <li><span>Ensure all AI solutions meet <strong>enterprise standards for data privacy, security, compliance, and governance</strong>.</span></li> <li><span>Act as a <strong>technical mentor and thought leader</strong>, setting best practices for LLM engineering and applied AI.</span></li> <li><span>Stay current with advances in LLMs, agent frameworks, AI infrastructure, and applied research—and translate them into pragmatic solutions.</span></li> </ul> <p><span> </span></p> <strong><span>Qualifications</span></strong> <p><span><strong>Basic Qualifications:</strong></span></p> <ul> <li><span><strong>Bachelor’s degree</strong> in AI, Machine Learning, Computer Science, Statistics, or a related field</span></li> <li><span><strong>A minimum of 8 years of professional experience</strong> in software engineering, AI, or machine learning, including a minimum of 3 years of <strong>significant hands‑on work on LLM‑based systems</strong>.</span></li> <li><span>Proven, production experience with <strong>Large Language Models</strong>, including: </span></li> <ul> <li><span>Prompt engineering and prompt optimization</span></li> <li><span>Model integration and orchestration</span></li> <li><span>Evaluation and reliability tuning</span></li> </ul> <li><span>Strong proficiency in <strong>Python</strong> and modern AI/ML frameworks and libraries (e.g., PyTorch, TensorFlow, LangChain, similar ecosystems).</span></li> <li><span>Solid background in <strong>deep learning and applied machine learning</strong>.</span></li> <li><span>Strong analytical and mathematical foundation relevant to ML systems.</span></li> <li><span>Experience designing systems that balance <strong>performance, scalability, cost, and accuracy</strong>.</span></li> <li><span>Ability to communicate complex technical concepts clearly to <strong>technical and non‑technical stakeholders</strong>.</span></li> <li><span>Strong written and spoken English.</span></li> </ul> <h2><span><strong>Preferred Qualifications:</strong></span></h2> <ul> <li><span><strong>Master’s degree</strong> in AI, Machine Learning, Computer Science, Statistics, or a related field (or equivalent professional experience).</span></li> <li><span><strong>PhD or additional advanced degree</strong> in AI, Machine Learning, Computer Science, Statistics, or related fields.</span></li> <li><span>Experience building <strong>meaningful visualizations</strong> and explaining model behavior and results.</span></li> <li><span>Background in <strong>data mining, analytics, or decision‑support systems</strong>.</span></li> <li><span>Experience with <strong>regression, supervised and unsupervised learning</strong>, and applied ML in production contexts.</span></li> <li><span>Prior experience with <strong>