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Full Stack AI Engineer

Job in Plano, Collin County, Texas, 75086, USA
Listing for: MAS Global Consulting
Full Time position
Listed on 2026-08-14
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Full Stack Developer, Backend Developer, Cloud Engineer - Software
Salary/Wage Range or Industry Benchmark: 140000 - 190000 USD Yearly USD 140000.00 190000.00 YEAR
Job Description & How to Apply Below

Career Opportunities with MAS Global Consulting

Are you ready for new challenges and new opportunities?

Current job opportunities are posted here as they become available.

A great place to work.

Who We Are

At MAS Global Consulting, we are a premium digital engineering partner trusted by innovative startups and Fortune 100 companies, delivering high-impact solutions through agile delivery, deep technical expertise, and a strong people-first culture across the Americas.

We build long-term partnerships, not just software. MAS means "more" in Spanish, and that mindset drives our commitment to greater opportunity, inclusion, and impact.

Founded by a Latina engineer from Medellín and headquartered in Tampa, Florida, MAS Global Consulting is a 100% Hispanic and woman-owned company, recognized as a Great Place to Work and one of the Fastest-Growing Companies in the US.

Who You Are

You are a Senior AI/ML Full Stack Engineer who brings deep hands-on experience building production-grade AI applications with Java or Python. You've spent 7-10+ years mastering full-stack development and have moved confidently into agentic AI, RAG architectures, and LLM orchestration. You're just as comfortable designing a vector store retrieval pipeline as you are hardening a CI/CD deployment on AWS. You thrive in fast-paced, in-office environments where live coding and hands-on problem solving are part of the culture, and you're excited to bring responsible AI practices — guardrails, evaluation frameworks, and content filtering — into everything you build.

What

You'll Do
  • Design and build AI/ML applications end-to-end, from architecture through production deployment
  • Implement RAG pipelines, including chunking strategies, embedding models, and vector store integration (Pinecone, Open Search, pgvector, FAISS)
  • Build and orchestrate AI agents using frameworks such as Lang Chain, Llama Index, Semantic Kernel, or CrewAI
  • Develop and deploy solutions using AWS Bedrock, Anthropic Claude models, and model invocation APIs
  • Apply advanced prompt engineering techniques — system prompts, few-shot, chain-of-thought, tool use, structured outputs
  • Build conversational AI experiences, including chatbots (text) and voicebots (speech-to-text, text-to-speech)
  • Design and maintain APIs (REST, GraphQL), microservices, and event-driven architectures
  • Own CI/CD pipelines, containerization (Docker, ECS/EKS), and infrastructure-as-code for production systems
  • Implement evaluation frameworks, guardrails, content filtering, and responsible AI practices across LLM-powered features
What You Bring
  • Bachelor's degree required
  • 10+ years of software development experience (Java or Python), OR 7+ years if entirely full-stack + Agentic AI development experience
  • 2+ years hands-on experience building AI/ML applications in production
  • Strong proficiency with RAG architectures — chunking strategies, embedding models, vector stores (Pinecone, Open Search, pgvector, FAISS)
  • Experience with AI orchestration frameworks:
    Lang Chain, Llama Index, Semantic Kernel, or CrewAI
  • Hands-on experience with AWS Bedrock, Anthropic Claude models, and model invocation APIs
  • Proven prompt engineering skills — system prompts, few-shot, chain-of-thought, tool use, structured outputs
  • Experience building conversational AI: chatbots (text) and voicebots (speech-to-text, text-to-speech)
  • Proficiency with AWS services (Lambda, Step Functions, API Gateway, S3, DynamoDB, SQS)
  • Experience with CI/CD pipelines, containerization (Docker, ECS/EKS), and infrastructure-as-code
  • Strong understanding of API design (REST, GraphQL), microservices architecture, and event-driven systems
  • Familiarity with evaluation frameworks for LLM outputs
  • Experience with guardrails, content filtering, and responsible AI practices
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