Cloud Architect

CLARITY TECHNOLOGY PARTNERS; Dallas (TX); On-site; USD 170,000 - 175,000; Full time

Benefits offered by this job

Medical, Vision, and Dental benefits

401k

PTO

Job summary

CLARITY TECHNOLOGY PARTNERS is looking for an AI Cloud Solutions Architect based in Dallas, TX. This role involves architecting end-to-end AI solutions and ensuring successful AI implementation for clients.

The ideal candidate will have at least 8 years in software engineering, with a strong background in AI and MLOps. Benefits include medical, dental, and vision coverage, along with a 401k plan and PTO.

Qualifications

• 8+ years in software engineering or data science with 3+ years in AI/ML architecture or technical lead role.
• Experience designing and deploying AI/ML systems on cloud platforms.
• Strong command of modern AI/ML tooling.

Responsibilities

• Architect end-to-end AI solutions from data ingestion to monitoring.
• Evaluate and select appropriate AI approaches for client problems.
• Design and oversee the build of AI/ML infrastructure.

Skills

Software engineering

Machine Learning

AI Architecture

Python

Data Science

MLOps

Cloud platforms (AWS, Azure, GCP)

Tools

Terraform

PyTorch

TensorFlow

Hugging Face

LangChain

LlamaIndex

Vector databases (Pinecone, Weaviate, pgvector)

Job description

Job Title: AI Cloud Solutions Architect

Location: Addison, TX – Onsite 4 days a week

The Company Headquartered in Dallas TX, our client is a technology and strategy consultancy that aims to provide a competitive edge for 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 technologies that include Azure, Terraform, Python, and MLOps.

Compensation

Expectation: $170,000 – $175,000

What You’ll Do

• AI System Design & Strategy
  Architect end-to-end AI solutions — from data ingestion and feature engineering through model training, evaluation, deployment, and monitoring.
• Evaluate and select appropriate AI approaches for client problems — supervised learning, LLMs, RAG, agentic systems, computer vision, NLP, and time-series forecasting.
• Technical Execution
  Design and oversee the build of AI/ML infrastructure: model serving layers, vector stores, embedding pipelines, orchestration frameworks, and feedback loops.
• Lead technical reviews of model performance, data quality, prompt engineering, fine‑tuning approaches, and inference optimization.
• Influence and Leadership
  Act as a trusted AI advisor to client executives and engineering teams — translating AI capabilities into business outcomes and setting realistic expectations.
• Build alignment across data, cloud, and application teams to ensure AI systems are well-integrated and operationally sound.
• Execution and Delivery
  Translate AI architecture decisions into clear implementation roadmaps, sprint plans, and measurable success criteria.
• Ensure AI systems meet governance standards: model cards, bias assessments, data lineage, version control, and audit trails.

Qualifications

• 8+ years in software engineering, data science, or ML engineering; 3+ years in a dedicated AI/ML architecture or technical lead role.
• Proven experience designing and deploying production AI/ML systems on cloud platforms (AWS, Azure, or GCP), including model serving, pipelines, and monitoring.
• Strong command of modern AI/ML tooling: Python, PyTorch or TensorFlow, Hugging Face, LangChain or LlamaIndex, and vector databases (Pinecone, Weaviate, pgvector).
• Hands‑on experience with LLM integration patterns: RAG, prompt engineering, fine‑tuning, function calling, and multi‑agent orchestration.
• Solid understanding of MLOps practices: experiment tracking (MLflow, W&B), CI/CD for models, model registries, drift detection, and A/B evaluation frameworks.
• Exceptional communication skills — able to explain AI system trade-offs clearly to both engineers and non‑technical stakeholders.
• Preferred: Experience deploying AI in regulated or operationally complex industries such as financial services, agriculture, logistics, or construction.
• Familiarity with responsible AI frameworks: fairness, explainability (SHAP, 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: feature stores, data lakes, streaming pipelines, and the data contracts that underpin reliable AI.
• Professional certifications (e.g., AWS Certified ML Specialty, Google Professional ML Engineer, Azure AI Engineer Associate, Deep Learning Specialization).

Benefits Offered

• Medical, Vision, and Dental benefits
• 401k
• PTO