AI Architect

CTP; Dallas (TX); On-site; USD 150,000 - 230,000; Full time

Benefits offered by this job

401k

PTO

Job summary

CTP, headquartered in Dallas, is seeking an AI Architect to design end-to-end AI solutions across data ingestion, feature engineering, model development, deployment, and monitoring. This role focuses on building scalable, compliant architectures using modern ML tooling and cloud platforms.

You will lead cross-functional teams, evaluate model performance and data quality, and drive implementation roadmaps while mentoring engineers and analysts in responsible AI and MLOps practices.

Qualifications

• 8+ years in software, data, or ML engineering with 3+ years leading AI/ML architecture initiatives

Responsibilities

• Architect AI solutions across the full lifecycle from data ingestion to monitoring.
• Assess client requirements and determine AI approaches including supervised ML, LLMs, RAG, agentic systems.
• Develop AI architecture blueprints balancing scalability, latency, cost, explainability, and regulatory requirements.

Skills

AI Architecture

AWS/Azure/GCP

MLOps

LangChain/LlamaIndex

PyTorch/TensorFlow/Hugging Face

LLM integration

Stakeholder Communication

Education

B.S. in Computer Science or related field

Tools

LangChain

LlamaIndex

Pinecone

Weaviate

pgvector

MLflow

Weights & Biases

Job description

Headquartered in Dallas TX, our client is a technology and strategy consultancy that aims to provide a competitive edge to its clients by solving complex problems with data, software, and strategy. They specialize in areas like technology strategy, product development, software engineering, and digital transformation, with a particular emphasis on AI, MLOps, and Data Engineering. The firm’s clientele spans various industries, including AgTech, Healthcare, Logistics, and Financial Services.

Platform / Stack

You will work with modern AI/ML technologies and cloud platforms, including AWS, Azure, or GCP, Python-based ML frameworks, LLM orchestration tools, vector and embedding infrastructure, and MLOps platforms.

What You’ll Do as an AI Architect

• Architect complete AI solutions across the full lifecycle, from data ingestion and feature engineering through model development, evaluation, deployment, and ongoing monitoring.
• Assess client requirements and determine the appropriate AI approach, including supervised ML, LLMs, RAG, agentic systems, computer vision, NLP, and time-series forecasting.
• Develop AI architecture blueprints that balance scalability, latency, cost, explainability, regulatory requirements, and business objectives.

Technical Execution

• Design and oversee production AI/ML infrastructure, including model serving, vector databases, embedding pipelines, orchestration frameworks, and feedback loops.
• Lead technical assessments of model performance, data quality, prompt engineering, fine-tuning strategies, and inference optimization.
• Evaluate trade-offs between model capabilities, operational complexity, maintainability, and time-to-value to ensure production-grade solutions.

Influence & Leadership

• Serve as a trusted AI advisor to client executives and technical teams, translating AI capabilities and architectural decisions into practical business outcomes.
• Collaborate across data, cloud, and application teams to ensure AI solutions are properly integrated into enterprise environments.
• Mentor engineers and analysts on AI best practices, responsible AI, and production MLOps while helping establish strong technical standards across client engagements.

Execution & Delivery

• Turn AI architecture decisions into actionable implementation roadmaps, sprint plans, and measurable success criteria.
• Ensure AI solutions follow appropriate governance practices, including model cards, bias assessments, data lineage, version control, and audit trails.
• Navigate evolving AI capabilities, changing client requirements, and production uncertainty while maintaining clear technical direction and delivery outcomes.

Qualifications:

You could be a great fit if you have:

• 8+ years of experience in software, data, or ML engineering, with 3+ years leading AI/ML architecture initiatives or equivalent depth building production LLM and agentic AI systems.
• Proven experience designing and deploying production AI/ML solutions on AWS, Azure, or GCP, including model serving, pipelines, and monitoring.
• Demonstrated ability to build and ship production AI/ML systems using modern tooling such as Python-based ML frameworks including PyTorch, TensorFlow, or Hugging Face; LLM orchestration frameworks such as LangChain or LlamaIndex; and vector/embedding infrastructure such as Pinecone, Weaviate, or pgvector. Specific tools are less important than demonstrated experience building, architecting, and operating these systems in production.
• Hands-on experience with LLM integration patterns including RAG, prompt engineering, fine-tuning, function calling, and multi-agent orchestration.
• Strong understanding of MLOps practices including experiment tracking with MLflow or W&B, model CI/CD, model registries, drift detection, and A/B evaluation frameworks.
• Exceptional communication skills with the ability to clearly explain AI architecture, technical trade-offs, and business implications to both technical and non-technical stakeholders.

Preferred

• Experience deploying AI solutions in regulated or operationally complex industries such as Financial Services, Agriculture, Logistics, or Construction.
• Knowledge of responsible AI practices including fairness, explainability using tools such as SHAP or LIME, privacy-preserving techniques, and model risk management.
• Exposure to edge AI, IoT sensor data, or real-time inference at scale.
• Understanding of data architecture fundamentals including feature stores, data lakes, streaming pipelines, and data contracts supporting reliable AI systems.
• Professional certifications such as AWS Certified ML Specialty, Google Professional ML Engineer, Azure AI Engineer Associate, or Deep Learning Specialization.
• Experience working at the forefront of agentic AI, including credentials such as Anthropic's Claude Certified Architect or hands-on exposure to emerging context and knowledge-sharing approaches for agents, such as Google’s Open Knowledge Format. This is considered a strong signal but is not required given the emerging nature of these technologies.

Benefits Offered

Employer provides access to:

• 401k
• PTO

This client requires that a background check be completed. A background check is required to protect our company/client and its stakeholders by ensuring that we hire individuals with a trustworthy history, which helps maintain a safe and secure workplace. This proactive measure minimizes potential risks and promotes a culture of integrity within the organization.