Senior AI Engineer

Project Overview

The Senior AI Engineer reports directly to the VP of AI and is responsible for leading and advancing enterprise-wide AI initiatives. This role focuses on designing, developing, and deploying end-to-end AI systems and intelligent agents that drive automation, decision-making, and business value at enterprise scale.

As a senior independent contributor, the engineer will work across multiple concurrent initiatives involving AI architecture, model integration, and developer tooling. The role requires close collaboration with Data Scientists, Data Engineers, product owners, and business stakeholders to translate complex requirements into production-ready AI solutions.

The ideal candidate has deep expertise in enterprise AI architecture and hands-on experience with AI coding assistants such as Claude Code and GitHub Codex to accelerate development while maintaining high engineering standards throughout the AI development lifecycle.

Key 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.
• Integrate models, LLMs, agentic services, expert systems, and knowledge graphs into cohesive AI solutions.
• Use AI coding assistants such as Claude Code, GitHub Codex, and similar tools to accelerate development, automate repetitive engineering tasks, and improve code quality.
• Build and maintain scalable AI pipelines using Databricks and AWS while integrating with existing data infrastructure and enterprise systems.
• Define and implement enterprise AI architecture standards, patterns, and best practices.
• Evaluate and integrate LLMs, foundation models, and generative AI capabilities into business applications.
• Collaborate with Data Scientists to operationalize ML models and transition experiments from prototype to production.
• Partner with cross-functional teams to scope, design, and deliver AI-powered solutions across multiple simultaneous initiatives.
• Establish model monitoring, evaluation, and feedback loops to maintain accuracy, safety, and performance in production.
• Stay current with developments in AI and recommend tools, frameworks, and approaches that improve outcomes.
• Mentor junior engineers and contribute to technical excellence, experimentation, and continuous learning.
• Prepare technical documentation, architecture diagrams, and executive presentations communicating AI strategy and results.

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, Mathematics, or a 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 experience building and deploying end-to-end AI systems and AI agents in production environments.
• Experience using AI coding assistants such as Claude Code, GitHub Codex, or equivalent tools within an active development workflow.
• Hands-on experience with Databricks for model training, feature engineering, and pipeline orchestration.
• Strong experience with AWS cloud services such as SageMaker, Lambda, S3, EC2, Step Functions, or equivalent services for AI/ML workloads.
• Strong Python skills, including experience with SparkSQL, MLlib, PyTorch, spaCy, and NLTK for NLP and ML development.
• Experience integrating AI systems through REST APIs, GraphQL, and OAuth for secure and scalable enterprise connectivity.
• Ability to work independently across multiple initiatives simultaneously without close supervision.
• Experience designing enterprise AI architecture, including APIs, orchestration layers, vector databases, and model-serving infrastructure.

Technical & Soft Skills

Preferred Technical Skills

• Experience building multi-agent systems and knowledge graphs using frameworks such as LangGraph, AutoGen, CrewAI, or the Anthropic Agent SDK.
• Familiarity with React/Native, Figma, Dash, or similar front-end and visualization technologies, as well as Bootstrap, for AI-powered user interfaces and data applications.
• Knowledge of 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 environments.
• Experience with Databricks Unity Catalog, Delta Lake, and MLflow for end-to-end model lifecycle management.

Soft Skills

• Strong communication and stakeholder management skills, with the ability to explain technical AI concepts clearly to engineering teams and business executives.
• Ability to evaluate build-versus-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.
• Strong attention to detail and a focus on delivering reliable, well-documented, production-grade systems.
• Ability to collaborate effectively across technical and business teams while managing multiple prioritie
  
  
  
  Duration: 1 year, Temp-to-Hire

Location Requirement: Remote Must be in the Miramar or Dallas area or willing to relocate. Client does not assist with relocation.