Offer summary

Qualifications:

At least 5 years of experience in machine learning or AI architecture roles., Proven expertise with LLM platforms like OpenAI GPT-4+, Hugging Face Transformers., Experience deploying ML/NLP/LLM systems in production environments., Knowledge of healthcare data privacy and regulatory requirements, such as HIPAA..

Key responsibilities:

Design and deploy AI systems aligned with healthcare data requirements.

Lead architectural decisions across AI workflows including training, deployment, and monitoring.

Develop and maintain NLP pipelines for clinical applications like summarization and semantic search.

Mentor engineering teams on best practices for reliable and responsible AI use.

Job description

About the Job

Loopback Analytics is seeking a seniorlevel AI Architect with realworld, handson experience deploying machine learning and natural language processing solutions, particularly in environments involving healthcare data. This is a role for a “been there, done that” builder — someone who’s led teams or projects involving LLMs, ML pipelines, and NLP workflows across real data with real constraints. You won’t just prototype models; you’ll design, build, and scale productiongrade AI systems that have immediate impact.

As a pivotal member of Loopback’s engineering team, this individual will be responsible for designing and deploying AI systems in a cohesive manner that aligns with Loopback’s existing data and software architectures. The ideal candidate will have a proven track record of taking AIMLenabled products from concept to market, preferably within the Healthcare industry.

Job Duties to Include

Through close collaboration with the Product and Engineering teams, develop and own products and services that leverage the latest artificial intelligence and machine learning technologies and techniques
Be a thought leader across the organization on AIML; champion education and upskilling initiatives
Keep a close pulse on AIML technology advancements, industry trends, best practices, and vendor ecosystems
Assess LLMNLPML solution feasibility via solution outlines, risk analysis, and POCs
Design or assess systems for enterprise healthcare requirements, including privacy and regulatory requirements
Estimate effort and cost for LLMNLPML solutions at varying detail levels
Present and advise on AI strategies given existing, emerging, and potential capabilities
Break down work and write specifications for annual, quarterly, and sprint planning
Build and maintain LLMNLPML training and evaluation datasets
Consult and advise on both external product and internal labor optimization AI use cases
Design and oversee the development of scalable ML and LLMbased systems for healthcare data applications
Lead architectural decisions across AI workflows: model training, deployment, monitoring, versioning, and lifecycle management
Build and improve NLP pipelines, including named entity recognition (NER), clinical summarization, and semantic search
Mentor engineers and data scientists in best practices for model reliability, safety, and performance
Establish guardrails for responsible AI use, including bias mitigation and patient data protection