AI Implementation Architect

Job Title: AI Implementation Architect

Location: Memphis, TN (Preferred) or Remote

Duration: 18 Months + Extensions (Long-term contract.)

Start Date: ASAP

Number of Openings: 1

Top Skills in order of preference.

AI Solution Design and Implementation

Machine Learning and Data Science Expertise

AI Architecture and Pipeline Planning

ESSENTIAL JOB FUNCTIONS Design and build data and AI infrastructure systems including model selection and versioning, indexing, feedback capture, monitoring, and performance.

Integration: Implement AI solutions that integrate seamlessly with existing business systems and enhance functionality. Work with systems and application owners to design complementary AI solutions.

Collaboration: Work with data scientists, enterprise architects, and stakeholders to develop and deploy AI models based on use cases and value.

Operations: Ensure scalability, reliability, and security of AI and data orchestration systems. Design systems to produce ethical, un-biased output.

Governance and Improvement: Monitor and improve AI systems based on performance metrics and user feedback. Implement governance and observation methodologies to provide transparency to model operations.

REQUIREMENTS Proven experience as a Data Engineer, AI Engineer, or similar role, with a focus on production, customer facing implementation of data orchestration and AI technologies.

Strong ability to translate use case and business domain functions to production applications o Proficiency in programming languages (Python, Java) and related AI/ML frameworks (ex. LangChain, TensorFlow, PyTorch).

Experience with prompt flows, prompt engineering best practices, and AI patterns (ex. Retrieval Augmented Generation (RAG)) o Development and production deployment experience with data products on public cloud platforms (Azure and AWS preferred) o Knowledge of vector databases, search indexes, and embeddings.

Experience with fine-tuning (PEFT) is a plus o Experience with API architecture and design is a plus.

Education: Bachelor's degree in Computer Science, Engineering, or a related field.

Certifications: Relevant certifications in AI, data engineering, or cloud computing are a plus.