Senior AI Engineer

Role: Senior AI Engineer

Location: Remote

The Senior AI Engineer reports directly to the VP of AI and is responsible for leading and advancing enterprise-wide AI initiatives. This role leads the design, development, and deployment of end-to-end AI systems and intelligent agents that drive automation, decision-making, and business value at enterprise scale. Operating as a senior independent contributor, this role works comfortably across multiple concurrent initiatives spanning AI architecture, model integration, and developer tooling while collaborating with Data Scientists, Data Engineers, product owners, and business stakeholders to translate complex requirements into production-ready AI solutions. With deep expertise in enterprise AI architecture and hands-on proficiency with AI coding assistants such as Claude Code and Codex, this individual accelerates delivery velocity while maintaining rigorous engineering standards across the full AI development lifecycle.

Duties and Responsibilities

Design, build, and deploy end-to-end AI systems from data ingestion and model development through inference, monitoring, and continuous improvement

Architect and develop AI agents and multi-agent frameworks capable of reasoning, planning, and executing complex workflows autonomously

Build cohesive AI solutions through the orchestration and integration of Models, LLMs, agentic services, expert systems, and knowledge graphs

Leverage AI coding assistants (Claude Code, GitHub Codex, and similar tools) to accelerate development, automate repetitive engineering tasks, and improve code quality across the team

Build and maintain scalable AI pipelines on Databricks and AWS, integrating with existing data infrastructure and enterprise systems

Define and implement enterprise AI architecture standards, patterns, and best practices across the organization

Evaluate and integrate large language models (LLMs), foundation models, and generative AI capabilities into business applications

Collaborate with Data Scientists to operationalize ML models and move experiments from prototype to production

Partner with cross-functional teams across multiple simultaneous initiatives to scope, design, and deliver AI-powered solutions

Establish model monitoring, evaluation, and feedback loops to ensure AI systems remain accurate, safe, and performant in production

Stay current with the rapidly evolving AI landscape and proactively recommend new tools, frameworks, and approaches that improve outcomes

Mentor junior engineers and contribute to a culture of technical excellence, experimentation, and continuous learning

Prepare technical documentation, architecture diagrams, and executive presentations to communicate AI strategy and results

Minimum Qualifications/Requirements

Bachelor's degree in Computer Science, Engineering, Mathematics, or related field; master's degree preferred

7+ years of experience in software or data engineering with at least 5 years focused on AI/ML systems development

Demonstrated end-to-end experience building and deploying AI systems and AI agents in production environments

Proficiency with AI coding assistants such as Claude Code, GitHub Codex, or equivalent tools as part of an active development workflow

Hands-on experience with Databricks for model training, feature engineering, and pipeline orchestration

Solid experience with AWS cloud services (SageMaker, Lambda, S3, EC2, Step Functions, or equivalent) for AI/ML workloads

Strong Python skills including SparkSQL, MLlib, PyTorch, spaCy, and NLTK for NLP and ML model development

Experience integrating AI systems via REST APIs, GraphQL, and OAuth for secure, scalable enterprise connectivity

Proven ability to operate as a senior independent contributor across multiple initiatives simultaneously without close supervision

Experience designing enterprise AI architecture including APIs, orchestration layers, vector databases, and model serving infrastructure

Preferred Skills

Experience building multi-agent systems and knowledge graphs using frameworks such as LangGraph, AutoGen, CrewAI, or the Anthropic Agent SDK

Familiarity with front-end and visualization technologies including React/Native, Figma, Dash or similar, and Bootstrap for building AI-powered user interfaces and data applications

Familiarity with prompt engineering, retrieval-augmented generation (RAG), and fine-tuning techniques for production LLM applications

Experience with MLOps practices including CI/CD for AI systems, model versioning, and automated evaluation pipelines

Knowledge of vector databases such as Pinecone, Weaviate, or pgvector for semantic search and retrieval applications

Familiarity with data governance, AI safety, and responsible AI principles in enterprise settings

Experience with Databricks Unity Catalog, Delta Lake, and MLflow for end-to-end model lifecycle management

Strong communication and stakeholder management skills able to present technical AI concepts clearly to both engineering teams and business executives

Ability to evaluate build vs. buy tradeoffs for AI tooling and make architecture recommendations with long-term maintainability in mind

Experience contributing to AI strategy, roadmap planning, and organizational AI adoption initiatives

Attention to detail with a strong bias toward shipping reliable, well-documented, production-grade systems