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The average salary range is between $ 78,967 and $ 135,731 year , with the average salary hovering around $ 97,500 year .
Senior Generative AI Engineer / Architect
BILL RATE 60-70
Division: Engineering
Location: Plano, Texas
Employment Type: Contract (1 Year)
Work Model: Hybrid (4 Days On-site per Week)
Experience Required: 5+ Years
Position Summary
We are seeking an experienced Senior Generative AI Engineer / Architect to design, develop, and prototype AI/ML solutions that solve complex business challenges and deliver measurable business value.
This role combines Generative AI, machine learning, data engineering, and AI solution architecture. The successful candidate will build AI-powered applications, develop proofs of concept (POCs), evaluate emerging technologies, and collaborate with engineering and architecture teams to transition validated solutions into scalable production systems.
The ideal candidate is a hands-on AI engineer with strong Python and SQL skills, experience building LLM applications, RAG systems, and AI agents, and the ability to solve ambiguous technical and business problems.
Key Responsibilities
AI/ML & Generative AI Development
Design, develop, and evaluate AI/ML solutions using machine learning, data science, statistical modeling, and Generative AI.
Build LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, vector search solutions, and agentic AI workflows.
Develop reusable data pipelines and AI workflows using structured and unstructured data.
Apply traditional machine learning and advanced AI techniques to improve business processes and operational efficiency.
Rapid Prototyping & Innovation
Build functional prototypes and POCs using Python, AI SDKs, and model APIs.
Design, test, and optimize AI workflows, retrieval systems, and agent-based applications.
Conduct experiments, evaluate model performance, and validate technical feasibility.
Research and evaluate emerging AI technologies, frameworks, and tools.
AI Architecture & Engineering
Collaborate with engineering teams to transition AI prototypes into production-ready solutions.
Contribute to architecture decisions involving model selection, APIs, cloud infrastructure, security, scalability, and deployment.
Support AI system integration with enterprise applications and data platforms.
Work with architects, product managers, data engineers, and business stakeholders to align AI solutions with business requirements.
Research & Stakeholder Collaboration
Monitor developments in Generative AI, machine learning, and open-source technologies.
Present technical findings, prototypes, and recommendations to stakeholders.
Document AI solutions, architectural decisions, and implementation approaches.
Support workshops, technical discussions, and knowledge-sharing initiatives.
Required Qualifications
Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related field, or equivalent experience.
5+ years of experience in AI engineering, machine learning, data science, data engineering, or related technical roles.
Strong programming skills in Python and SQL.
Hands-on experience developing ML models, LLM applications, RAG pipelines, vector search solutions, and AI-enabled workflows.
Experience with AI/ML frameworks, libraries, and model APIs.
Experience with platforms such as Azure AI, OpenAI, Anthropic, AWS Bedrock, Databricks, or Hugging Face.
Strong understanding of data pipelines, model experimentation, and AI solution development.
Excellent analytical, problem-solving, communication, and documentation skills.
Preferred Qualifications
Experience with MLOps, LLMOps, model evaluation, monitoring, and responsible AI practices.
Experience designing scalable AI architectures and deploying solutions in cloud environments.
Experience with agentic AI, multi-agent systems, workflow orchestration, and fine-tuning.
Knowledge of enterprise AI integrations, APIs, and production deployment patterns.
Experience with enterprise analytics and data engineering solutions.
Relevant AI, cloud, or data engineering certifications.
Tools & Technologies
Programming: Python, SQL
Generative AI: LLMs, RAG, AI Agents, Vector Search, Model APIs
AI Platforms: Azure AI, OpenAI, Anthropic, AWS Bedrock, Hugging Face
Data & ML: Databricks, Machine Learning, Data Pipelines, Statistical Modeling
Architecture: Cloud Services, APIs, System Integration, Scalable AI Deployment
Operations: MLOps, LLMOps, Model Evaluation and Monitoring
Ideal Candidate Profile
The ideal candidate is a hands-on Generative AI Engineer with strong software development and AI/ML expertise. Strong candidates will have experience building LLM applications, RAG systems, AI agents, and production-oriented AI solutions, along with the architectural knowledge to design scalable enterprise AI systems.
The candidate should be comfortable experimenting with emerging technologies, solving complex business problems, and collaborating with technical and business stakeholders to deliver practical AI solutions.
