AI Architect — Dallas, TX

The Company

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

AI System Design & Strategy

• 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:

• Medical, Vision, & Dental benefits

• 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.