Job Description:The Principal AI Engineer provides technical leadership in the design, development, training, orchestration, and operationalization of enterprise AI systems. This role defines strategy and technical standards across traditional AI/ML models, large language models, model training and fine-tuning, data and retrieval systems, and agentic AI architectures. The Principal AI Engineer remains deeply hands-on while partnering across product and platform teams to build scalable, reliable, secure, and high-quality AI capabilities.
Essential Job Functions and Responsibilities- Define and own technical strategy across enterprise AI systems, spanning traditional AI/ML models, large language models, model training and fine-tuning, retrieval, orchestration, and agentic AI.
- Design, train, fine-tune, evaluate, and optimize traditional AI/ML models and large language models using parameter-efficient techniques (e.g., QLoRA, LoRA, PEFT) and full fine-tuning where warranted.
- Design orchestration and abstraction layers that connect LLMs to enterprise tools, data, APIs, and specialized sub-agents while decoupling product teams from underlying models and providers.
- Design, build, and operate MCP (Model Context Protocol) servers and establish standards for how AI tools and capabilities are defined, exposed, versioned, and consumed.
- Design and optimize retrieval (RAG) systems, including chunking strategies, embedding models, vector stores, hybrid/keyword search, re-ranking, and context assembly.
- Design and optimize data pipelines for ingestion, cleaning, labeling, feature engineering, storage, versioning, and reuse of training, validation, and test datasets.
- Build reproducible AI/ML pipelines and establish standards for experiment tracking, dataset management, model versioning, checkpoints, metrics, and evaluation.
- Define evaluation methodologies and benchmarks across model, retrieval, and orchestration quality, including regression testing, retrieval precision/recall, tool-selection accuracy, task success, latency, and cost.
- Build routing, context-window management, memory, observability, and tracing strategies for multi-step and agentic AI workflows.
- Establish practices for AI data governance, lineage, quality, licensing/consent, PII handling, safety, guardrails, authentication, and access control.
- Partner with product and platform teams to operationalize AI capabilities, onboard tools and agents, and transition trained and fine-tuned models into production.
- Mentor engineers, establish technical standards and best practices, and raise AI engineering maturity across teams.
Knowledge, Skills, and Abilities- Strong hands-on experience designing, training, fine-tuning, and deploying traditional AI/ML models and large language models in production environments.
- Deep experience with parameter-efficient fine-tuning (QLoRA, LoRA, PEFT), quantization, model evaluation, and the tradeoffs between fine-tuning approaches.
- Deep hands-on experience with LLM orchestration, agentic AI patterns, MCP servers, tool/function calling, and frameworks such as LangGraph, LlamaIndex, Semantic Kernel, or equivalents.
- Deep experience with retrieval/RAG architectures, including chunking strategies, embeddings, vector databases, hybrid search, re-ranking, and context assembly.
- Proficiency with ML/DL frameworks and libraries such as PyTorch, Hugging Face Transformers/PEFT/TRL, and scikit-learn.
- Experience building and operating scalable data and AI pipelines and platforms using technologies such as Spark, Ray, dbt, or equivalents.
- Strong understanding of AI/ML data management, including dataset storage architecture, versioning, lineage, governance, quality, and PII handling.
- Experience with AI/ML evaluation, experiment tracking, reproducibility, observability, distributed training, and compute, cost, and latency optimization.
- Ability to define enterprise AI strategy, architecture, technical standards, and best practices while remaining hands-on in code.
- Strong stakeholder collaboration, technical leadership, mentoring, and problem-solving skills.
Education and Experience- Bachelor's degree in Computer Science, Engineering, or related discipline; advanced degree in ML, AI, or Data Science preferred
- 12 or more years of experience in AI/ML engineering, applied ML, or data engineering, with significant hands-on model training and fine-tuning
DisclaimerThe above statements describe the general nature and level of work performed in this role. Other duties may be assigned.
COMMENTS:The above statements are intended to describe the general nature and level of work being performed by individuals in this position. Other functions may be assigned, and management retains the right to add or change the duties at any time.
Pay Transparency Statement:US Base Salary Range: $159,600.00 - $207,500.00
Base pay offered to new hires may vary based upon factors including relevant industry and job-related skills and experience, geographic location, and business needs.* The range displayed does not encompass the full potential of the role, which allows for further growth and career progression.
In addition, as a part of our total compensation package, this role may be eligible for the Vertex Bonus Plan (VOB), a role-specific sales commission/bonus, and/or equity grants.
Learn more about Life at Vertex and connect with your recruiter for more details regarding Vertex's compensation and benefit programs.
*In no case will your pay fall below applicable local minimum wage requirements.
