Our valued healthcare client is seeking an intermediate AI Solution Engineer to implement a proof-of-concept solution that leverages AI enabled technologie

Our valued healthcare client is seeking an intermediate AI Solution Engineer to implement a proof-of-concept solution that leverages AI enabled technologies.

Initial 5-month contract with strong possibility of extension. 100% Remote in EST. 7.25 hours per day, Monday to Friday.

Project Details:

Our client is seeking a skilled AI solution engineer to implement a proof-of-concept (PoC) solution that leverages AI enabled technologies to automate/semi-automate interpretation and mapping data from disparate data sources into a standardized data model.The POC will focus on exploring the use of prompt-based AI systems for automating data mapping specification creation. This project will leverage large language models (LLMs) and data engineering principles to translate known source and target schemas, along with data coding standards, into structured mapping documentation in which can be used for ETL implementation.

Responsibilities:

• Develop a prompt-driven AI system capable of generating draft data mapping specifications between known source and target data schemas.
• Collaborate with our client's architecture team to architect the proof-of-concept pipeline integrating: Schema ingestion and parsing (e.g., from JSON, XML, DDL) and LLM-based reasoning and prompt workflows for mapping suggestions, using CIHI’s data standards and coding frameworks as reference.
• Fine-tune and evaluate LLM prompts or small-scale models (e.g., OpenAI, Azure OpenAI, or Hugging Face models) for semantic schema alignment.
• Implement a sample data pipeline for schema comparison, validation, and mapping metadata extraction.
• Integrate structured data transformations (e.g., SQL, Python-based ETL) with AI-assisted output.
• Develop reusable components that can be extended to other mapping or metadata automation tasks.
• Collaborate with the architecture team to define success metrics for the proof-of-concept (accuracy, consistency, and interpretability of AI-generated mappings).
• Document architecture, data flow, prompt design, and model behavior to support evaluation and knowledge transfer.
• Work with data standards, ETL, and analytics teams to understand domain-specific coding (e.g., ICD, CCI, or other healthcare data standards).
• Provide technical insight into integrating AI tools with metadata repositories or data integration environments.
• Present findings, prototypes, and recommendations to technical and non-technical stakeholders.

Must Haves:

• 3+ years of experience with AI/ML solution design and implementation, including use of LLMs (large language model) for semantic reasoning, ontology alignment, and data mapping.
• 3+ years of experience with Python and AI/ML frameworks (such as TensorFlow, PyTorch, Hugging Face, etc.).
• 3+ years of experience with cloud-native AI services (preferably AWS).
• 3+ years of experience integrating AI models into enterprise applications.
• 3+ years of experience understanding of security, compliance, and data residency requirements.
• 3+ years of experience with prompt engineering and fine-tuning LLMs for domain-specific tasks.
• 3+ years of experience with exposure to LLM orchestration frameworks (e.g., LangChain) for chaining model calls and retrieval-augmented generation.

Nice to have:

• Knowledge of AWS Bedrock and use of foundation models.
• Familiarity with health data coding standards (ICD-10, CCI, SNOMED CT).
• Hands-on experience with modern data engineering tools (ETL pipelines, APIs, relational and NoSQL databases).
• Hands-on experience with infrastructure as code (Terraform, AWS CDK, or CloudFormation).
• Experience with CI/CD pipelines (e.g., AWS CodePipeline, GitLab CI/CD, Jenkins).