Salary Range: 189500 to 189500 (Currency: USD) (Pay period: per-year-salary)

Requirements

• 5+ years as an AI/ML Engineer with a focus on healthcare AI/ML use cases.
• 5+ years of experience with Python and machine learning frameworks such as scikit-learn, SparkML, TensorFlow, PyTorch, pandas, Hugging Face, etc.
• Strong understanding of machine learning algorithms (e.g., supervised and unsupervised learning, natural language processing, reinforcement learning).
• Proven track record of applying ML techniques to healthcare data, particularly with natural language processing (NLP), Generative AI, and large language models (LLMs).
• Hands-on experience in training, tuning, and deploying models in production environments, including proficiency in advanced prompting techniques and fine-tuning LLMs for various use cases.
• Hands-on experience deploying machine learning models in cloud environments (preferably GCP, but AWS or Azure are acceptable).Expertise in building and scaling end-to-end machine learning pipelines in production environments.
• Familiarity with MLOps practices for model management and deployment.
• Excellent communication skills to convey complex technical concepts to non-technical stakeholders and to collaborate effectively within a cross-functional team.
• Strong expertise in data preprocessing, feature engineering, and model evaluation techniques.
• Ability to translate business requirements and metrics into machine learning model specifications and solutions.


Nice to Have:
• Experience working in distributed systems-based architectures, developing APIs, and implementing/deploying scalable backend services.
• Hands-on experience in data engineering, orchestration, ETL, and distributed unstructured data processing.
• Experience with cloud infrastructure, including Docker/Kubernetes deployments, security, and cost optimization.
• Knowledge of healthcare standards such as HL7, FHIR, or HIPAA compliance.

Job Responsibilities

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Model Design & Development:
• Collaborate with engineers to develop, modify, and optimize machine learning models, including both generative AI (LLMs) and discriminative AI models, tailored to address specific business challenges.
• Leverage large language models (LLMs) for applications such as text generation, text classification, and other AI-powered solutions.
• Design and implement models for predictive analytics and classification tasks, ensuring high accuracy and reliability.
• Design scalable, production-ready AI/ML solutions, taking models from initial concept through to deployment.
• Monitor and maintain models post-deployment, making necessary adjustments to improve performance and address changing requirements.
• Conduct experiments and fine-tune machine learning models to optimize their accuracy and overall performance.
• Create high-level and detailed design plans for AI/ML production solutions, including selecting appropriate algorithms, data sources, infrastructure, and technologies that align with the organization’s goals and constraints
Infrastructure, Scaling & Deployment:
• Design and implement scalable AI/ML pipelines that can efficiently handle production-level data and adapt to various use cases.
• Ensure successful deployment of models into production environments, focusing on stability, reliability, and seamless integration.
• Continuously track the performance of AI/ML solutions in production, addressing any issues, identifying model drift, and making necessary optimizations.
• Manage and automate model evaluation, training, and deployment processes using cloud infrastructure, with a focus on GCP (experience with AWS or Azure is also acceptable).
• Fine-tune machine learning models to maximize performance and scalability, ensuring they meet diverse and evolving user needs.
Problem-Solving & Business Impact:
• Understand both company and customer challenges, leveraging AI capabilities to develop innovative solutions that address these problems.
• Ensure the development and deployment of scalable, efficient, and high-quality AI solutions that meet business needs.
Technical Expertise & Architecture:
• Participate in design, architecture, and code reviews. Foster collaboration within the team, ensuring high-quality code standards are maintained while guiding the team through technical challenges and roadmap deliverables.
• Design and build efficient, resilient machine learning platforms and software products capable of scaling to meet production demands.
• Adhere to best practices for data privacy and security, ensuring full compliance when working with sensitive data.
• Actively seek opportunities to enhance and upgrade AI/ML infrastructure, tools, and solutions.
• Improve best practices for machine learning engineering by producing high-quality code, documentation, automated tests, and precise monitoring systems.

Within 1 Month You’ll:

• Gain a foundational understanding of our product, customers and patients
• Meet key internal stakeholders and begin to understand policies and protocols
• Establish rapport with existing Engineers across various product teams
• Build out basic understanding of existing AI applications within CareMessage

Within 3 Month You’ll:

• Gain a strong understanding of our technical environment and identify areas for growth in our processes, systems and/or tooling for AI/ML development
• Reviewed existing usage of AI, improved monitoring/performance capabilities, and suggested optimizations to the model.
• Identified, documented, and received approval for a proposed technical solution for use case in conjunction with the product team for the application of AI/ML.

Within 6 Month You’ll:

• Have a deep understanding of the product platform, our AI/ML needs and work in conjunction with management to refine the plan to meet our long term AI/ML goals.
• Have defined and chosen the appropriate tools and frameworks to support ML/AI model building, training, and deployment.
• Have developed an initial model to solve for the use case identified in the first 3 months.