Associate AI/ML Engineer

• Build and maintain end-to-end ML pipelines (data preparation, feature engineering, model training, evaluation, and deployment)
• Work with healthcare data such as EHR/EMR, claims, and clinical text to generate insights and support analytics solutions
• Apply AI/ML techniques including NLP, deep learning, and predictive modeling to solve business and clinical problems
• Collaborate with cross-functional teams to translate requirements into scalable AI-driven solutions
• Communicate model outcomes and analytical insights to stakeholders in a clear and structured manner
• Adhere to engineering standards, MLOps practices, and healthcare compliance requirements (e.g., HIPAA)
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regard to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Builder Responsibilities:
Design, develop, and deploy AI-powered solutions using no-code, low-code, and advanced platforms, translating business needs into scalable applications that enhance products, workflows, and decision-making.

Required Qualifications:

• Bachelor degree in Computer Science, AI/ML, Data Science, Statistics, or related field
• 1+ years of experience in machine learning, AI development, GenAI, RAG or data-driven engineering roles
• Proficiency in Python and experience with ML frameworks such as Scikit-learn, TensorFlow, or PyTorch
• Solid understanding of machine learning fundamentals, statistics, and data modeling concepts
• Experience with data tools such as Pandas, NumPy, and SQL

Required Qualifications:

• Familiarity with cloud platforms (Azure/AWS/GCP) and basic deployment concepts
  • Exposure to healthcare data/systems (EHR, claims, clinical datasets)