AI Architect - Full Time - Texas

Role: AI Architect

Location: Austin, TX – Hybrid

Duration: Full Time

Job Description

·      We are seeking a highly experienced and visionary AI Architect to lead the design, development, and governance of enterprise-scale AI and

·      machine learning solutions. In this role, you will define the technical direction for AI/ML platforms, oversee the adoption of Large Language

·      Models (LLMs) and Agentic AI systems, and collaborate with cross-functional teams to deliver intelligent, scalable, and responsible AI solutions

·      aligned with business objectives.

Technical Skills Summary

Category: Skills

Languages: Python, SQL, Scala, R

ML Frameworks: PyTorch, TensorFlow, Scikit-learn, Hugging Face, JAX

LLM / GenAI: GPT-4, Claude, LLaMA, Mistral, Gemini, RLHF, LoRA

Agentic AI: LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel

MLOps: MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML

Cloud Platform(s): AWS, Azure, GCP

Vector Database(s): Pinecone, ChromaDB, FAISS, Weaviate

Data Engineering: Spark, Kafka, dbt, Airflow

DevOps/Infra: Docker, Kubernetes, Terraform, CI/CD

Key Responsibilities

Architecture & Design

·         Define and own the enterprise AI/ML architecture strategy, including model development pipelines, MLOps platforms, and LLM integration patterns

·         Design scalable, secure, and maintainable AI systems leveraging cloud-native services (AWS, Azure, GCP)

·         Architect Retrieval-Augmented Generation (RAG) systems, vector database solutions, and knowledge graph integrations

·         Establish architectural patterns for Agentic AI systems including multi-agent orchestration, tool use, memory management, and autonomous workflows

·         Lead technical design reviews and ensure alignment with enterprise standards, security policies, and compliance requirements

LLM & Generative AI

· Evaluate, select, and integrate LLMs (e.g., GPT-4, Claude, Gemini, LLaMA, Mistral) for enterprise use cases

· Architect fine-tuning pipelines (LoRA, QLoRA, PEFT) for domain-specific model adaptation

· Define prompt engineering standards, guardrails, and output validation frameworks

· Oversee responsible AI practices including bias detection, hallucination mitigation, and explainability

MLOps & Platform Engineering

· Design end-to-end MLOps pipelines covering data ingestion, model training, evaluation, deployment, monitoring, and retraining

· Establish CI/CD practices for ML models and AI applications

· Define model registry, versioning, and governance standards

· Select and integrate ML platforms (e.g., MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI)

Agentic AI Systems

· Architect multi-agent frameworks using tools such as LangGraph, AutoGen, CrewAI, and Semantic Kernel

· Define agent orchestration patterns, tool-use boundaries, and human-in-the-loop approval workflows

· Establish security controls for agentic systems including prompt injection prevention and privilege separation

· Drive adoption of Model Context Protocol (MCP) and emerging agentic standards

Leadership & Collaboration

· Serve as the technical authority and subject matter expert for AI/ML

· Mentor and guide a team of ML engineers, data scientists, and AI developers

· Partner with product, data, security, and business stakeholders to translate requirements into AI solutions

· Present architectural decisions, trade-offs, and roadmaps to executive leadership

· Stay current with AI research, emerging frameworks, and industry trends; drive continuous innovation

Required Qualifications ·

Education: Bachelor's or Master's degree in Computer Science, Data Science, Electrical Engineering, or a related field ·

Experience: 10 years in software engineering or data science; 5 years in AI/ML architecture roles ·

Deep expertise in machine learning, deep learning, and statistical modeling · Hands-on experience with LLMs (GPT, Claude, LLaMA, Mistral) and generative AI application development

· Strong proficiency in Python; experience with TensorFlow, PyTorch, Scikit-learn, and Hugging Face

· Solid understanding of Transformer architecture, attention mechanisms, and NLP fundamentals

· Experience designing RAG pipelines with vector databases (Pinecone, ChromaDB, Weaviate, FAISS) · Proficiency with cloud AI services on AWS (SageMaker, Bedrock), Azure (OpenAI, ML Studio), or GCP (Vertex AI)

· Strong knowledge of MLOps practices: MLflow, Kubeflow, model monitoring, feature stores · Familiarity with agentic AI frameworks: LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel · Experience with containerization and orchestration: Docker, Kubernetes

· Understanding of data engineering principles: ETL, data lakes, streaming pipelines (Kafka, Spark)