Senior AI Developer/Engineer

Connect for Health Colorado•Denver, CO

45d•$128,750 - $160,680•Remote

About The Position

Connect for Health Colorado is seeking a Senior AI Developer/Engineer to join their Architecture team, reporting to the Chief Innovation Officer (CIO). This role focuses on identifying, prototyping, and maturing emerging technologies, particularly in AI, machine learning, and generative AI, to enhance business processes and operational efficiency. The position involves leading proof-of-concept development, translating concepts into requirements, and collaborating with various teams (BI, Security, Data, Application) to design scalable, secure, and compliant solutions. The role will also build reference architectures and semantic/domain models for AI services across AWS, Amazon Q, Snowflake Cortex, and Streamlit applications.

Requirements

Colorado resident.
7+ years of software engineering experience, including 3+ years delivering AI/ML and generative AI solutions in production environments.
Proficiency in Java; experience building APIs and lightweight applications using frameworks such as FastAPI/Flask and Streamlit.
Experience with AI/ML libraries and LLM tooling/evaluation practices (e.g., LangChain/LlamaIndex).
3+ years of AWS experience, including IAM/security, networking, logging/monitoring, and data services (Amazon Aurora/RDS PostgreSQL).
Experience with Snowflake, including Cortex and secure data access patterns; Snowpark experience is a plus.
Experience with Amazon Q and RAG patterns (embeddings/vector search) and semantic/domain modeling.
Knowledge of CI/CD pipelines, infrastructure-as-code, and automated testing for AI-enabled applications.
Strong problem-solving and communication skills; ability to work under deadlines and during incident response/on call; ability to translate concepts into clear requirements; bachelor’s degree (or equivalent experience) preferred.

Nice To Haves

Snowpark experience is a plus.

Responsibilities

Design, develop, and deploy AI, machine learning, and generative AI solutions to solve complex business challenges and enhance operational workflows.
Partner with the Chief Innovation Officer (CIO) and stakeholders to identify, prioritize, and scope AI/ML/GenAI use cases; define success criteria and proof-of-concept plans.
Prototype and deliver proofs-of-concept for emerging AI technologies, including large language models (LLMs), natural language processing (NLP), retrieval-augmented generation (RAG), and predictive analytics.
Translate validated proofs-of-concept into clear functional and non-functional requirements, user stories, acceptance criteria, and operational runbooks for production delivery.
Build and maintain semantic/domain models (taxonomies, ontologies, and metadata standards) to improve AI search, retrieval, and analytics across Exchange data.
Develop AI-enabled applications and demos using Streamlit and APIs (e.g., FastAPI/Flask) to support internal users and operational teams.
Implement solutions that leverage AWS services (including Amazon Aurora/RDS PostgreSQL), Amazon Q, and Snowflake Cortex, and integrate with existing applications and infrastructure.
Collaborate with the BI, Architecture, and Security teams to integrate AI solutions with existing systems, providing scalability, security, and compliance with organizational standards.
Collaborate with the QA department to select AI-related testing tools and frameworks (prompt/model evaluation, regression testing) to enhance testing efficiency and accuracy.
Develop and implement automation solutions for the QA department to streamline testing processes for AI-enabled applications.
Develop and maintain APIs and microservices to support AI-driven applications and their integration with SaaS platforms.
Implement and optimize machine learning pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment.
Work with data engineers to ensure high-quality data pipelines for training, evaluation, and deploying AI models.
Create and maintain detailed documentation for AI models, processes, and workflows to support internal teams and ensure knowledge transfer.
Build and manage CI/CD pipelines for automated deployment and testing of AI solutions.
Monitor and optimize the performance and cost of AI systems, including troubleshooting and resolving issues in production environments.
Ensure AI solutions comply with security, privacy, and Standard Operating Procedures (SOPs), including responsible AI guardrails and audit logging.
Provide technical guidance and mentorship on AI/ML best practices and provide emergency response for AI system issues through on-call support and monitoring escalations.