AI Developer

CareSource

17h

About The Position

Job Summary: The AI Developer plays a key role in designing, developing, and deploying intelligent solutions. This role is focused on solving complex business challenges through innovative AI technologies and will collaborate closely with cross-functional teams to ensure timely and high-quality delivery of solutions aligned with defined objectives. DS/Gen/Agentic AI resources with good hands-on skills on: Current Advancements: Generative AI, Agentic AI, LLM fine-tuning, RAG, GraphRAG, Vector databases, Knowledge Graph, Langchain, Langgraph, etc Model Deployment: MLOPs pipeline, CI/CD, Model Deployment, Docker, Kubernetes, Azure Cloud, Github, etc Core Skills: Traditional AI / ML / Data Science skillsets (Predictive, Prescriptive, Descriptive, Statistical, Optimization, Simulation, Natural Language processing, Computer Vision, Image Processing, etc) Domain: Healthcare, Facets, GuidingCare, etc Note: Domain skills are nice to have and not mandatory. Essential Functions: Design and implement AI models and algorithms tailored to diverse business challenges Define and lead the architecture of Generative AI platforms, including large language models (LLMs), vector databases, and inference pipelines Maintain deep expertise in modern generative AI technologies such as Knowledge Graphs, OpenAI, LLaMA, Python, LangChain, vectorization, embeddings, semantic search, Retrieval-Augmented Generation (RAG), Infrastructure as Code (IaC), and Streamlit Rapidly prototype proof-of-concept solutions to assess emerging technologies and innovative ideas Foster innovation and collaboration in a fast-paced environment through a hands-on, imaginative approach and a self-driven, inquisitive mindset Leverage AI-assisted development tools, including GitHub Copilot and internally developed solutions, to enhance productivity Apply creative problem-solving techniques to identify and implement process improvements Assess technical risks and develop effective mitigation strategies to ensure successful project delivery Collaborate with data scientists, software engineers, and product teams to integrate AI capabilities into production-ready systems Partner with leadership to evaluate existing services and develop strategies to optimize delivery and support Perform data preprocessing and analysis on large datasets to uncover actionable insights Train, validate, and fine-tune machine learning and deep learning models for optimal performance Deploy models using cloud infrastructure and containerization technologies such as Docker and Kubernetes Implement and manage MLOps pipelines to automate model training, deployment, monitoring, and lifecycle management Apply AIOps practices to enhance operational efficiency, automate incident detection, and optimize system performance using AI-driven insights Continuously monitor model performance and retrain as needed to maintain accuracy and relevance Stay current with industry trends, tools, and frameworks, and assess their applicability to organizational goals Document workflows, models, and codebases to support maintainability and knowledge sharing Provide timely and transparent progress updates to stakeholders, highlighting key milestones, challenges, and proposed solutions Perform any other job duties as requested

Requirements

Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence or related field, or equivalent years of relevant work experience is required
Minimum of five (5) years of experience in developing and deploying AI/ML models is required
Experience with cloud platforms (AWS, Azure, GCP) and MLOps tools Is required
Experience with Agile methodologies is required
Knowledge of model interpretability and ethical AI practices
Proficiency in Python and libraries such as TensorFlow, PyTorch, Scikit-learn, and OpenCV
Strong analytical, evaluative and problem-solving abilities
Strong understanding of data structures, algorithms, and software engineering principles
Excellent problem-solving skills and attention to detail
Strong communication and collaboration abilities

Nice To Haves

Master’s degree is preferred
Knowledge of healthcare and managed care
AI / Data Science certifications or credentials are preferred

Responsibilities

Design and implement AI models and algorithms tailored to diverse business challenges
Define and lead the architecture of Generative AI platforms, including large language models (LLMs), vector databases, and inference pipelines
Maintain deep expertise in modern generative AI technologies such as Knowledge Graphs, OpenAI, LLaMA, Python, LangChain, vectorization, embeddings, semantic search, Retrieval-Augmented Generation (RAG), Infrastructure as Code (IaC), and Streamlit
Rapidly prototype proof-of-concept solutions to assess emerging technologies and innovative ideas
Foster innovation and collaboration in a fast-paced environment through a hands-on, imaginative approach and a self-driven, inquisitive mindset
Leverage AI-assisted development tools, including GitHub Copilot and internally developed solutions, to enhance productivity
Apply creative problem-solving techniques to identify and implement process improvements
Assess technical risks and develop effective mitigation strategies to ensure successful project delivery
Collaborate with data scientists, software engineers, and product teams to integrate AI capabilities into production-ready systems
Partner with leadership to evaluate existing services and develop strategies to optimize delivery and support
Perform data preprocessing and analysis on large datasets to uncover actionable insights
Train, validate, and fine-tune machine learning and deep learning models for optimal performance
Deploy models using cloud infrastructure and containerization technologies such as Docker and Kubernetes
Implement and manage MLOps pipelines to automate model training, deployment, monitoring, and lifecycle management
Apply AIOps practices to enhance operational efficiency, automate incident detection, and optimize system performance using AI-driven insights
Continuously monitor model performance and retrain as needed to maintain accuracy and relevance
Stay current with industry trends, tools, and frameworks, and assess their applicability to organizational goals
Document workflows, models, and codebases to support maintainability and knowledge sharing
Provide timely and transparent progress updates to stakeholders, highlighting key milestones, challenges, and proposed solutions
Perform any other job duties as requested