Agentic AI Engineering

Design, develop, test, deploy, and maintain machine learning solutions, predictive analytics, recommendation systems, and intelligent automation.

Build Generative AI applications using LLM capabilities, RAG, and conversational AI, integrating models into enterprise applications and workflows.

Improve engineering delivery infrastructure, including merge gates, migration safety, canary deployments, rollback, and production troubleshooting.

Implement guardrails, auditability, and fail-closed checks for agent-enabled patient-care workflows.

Own warehouse and analytics pipelines supporting training, evaluation, deployment, monitoring, retraining, and model lifecycle management.

Collaborate on cloud deployment and scaling, architecture reviews, technical documentation, operational procedures, and the AI roadmap.

Enforce security, privacy, compliance, and responsible AI practices, including transparency, explainability, and fairness.

Demonstrated ability to deliver independently using a fleet of AI agents, with practical examples of supervised and unsupervised workflows beyond occasional Copilot use.

Broad architecture ownership across organizations or codebases, supported by sound engineering judgment and accountability for outcomes.

A proactive written communication style and the ability to investigate, decide, implement, and document without waiting for narrowly scoped tickets.

Strong analytical thinking and collaboration skills to translate technical decisions into business-focused recommendations., Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Mathematics, Statistics, Information Technology, or a related field.

At least 4 years of software engineering or systems architecture experience, with end-to-end systems ownership.

Experience developing, deploying, and supporting machine learning models and AI-driven applications.

Experience with cloud platforms such as Microsoft Azure, Amazon Web Services (AWS), or Google Cloud Platform (Google Cloud Platform).

Familiarity with model governance, monitoring, cloud-native AI services, distributed computing, scalable architecture, and cybersecurity.

Preferred Qualifications

Experience with large language models, generative AI platforms, and natural language processing tools.

Experience with MLOps, DevOps, CI/CD pipelines, containerization, and model deployment.

Healthcare or clinical research experience involving electronic health records, claims, wearables, or remote patient monitoring.

Thinking Minds’ client offers meaningful healthcare technology challenges, architectural ownership, and opportunities to shape responsible AI delivery.