AIML Engineer(Expert Level)

Product Development Position Overview We are seeking a highly skilled Expert level AI/ML Engineer to design, build, and operate autonomous AI/ML systems (Agentic AI) that monitor, decide, and act across cloud and application environments in the US Healthcare domain, specifically supporting Revenue Cycle Management (RCM), clinical, and operational workflows. This role focuses on developing intelligent agents leveraging: GenAI/LLM stacks (Transformers, HuggingFace, LangChain) Natural Language Processing (NLP) for clinical and administrative data Computer Vision for document and medical imaging analysis Predictive analytics & recommender systems Multi-agent orchestration Big Data pipelines (Spark, Hadoop, EMR, Redshift, BigQuery, Databricks, Kafka) .NET Core APIs RPA platforms (UiPath, Automation Anywhere) Cloud-native architectures (AWS, Azure, GCP) The engineer will deliver predictive, self-healing, and adaptive cloud operations for healthcare organizations, integrating advanced AI/ML frameworks to enable safe, explainable, and autonomous AI systems, while ensuring compliance with HIPAA, GDPR, and SOC 2. Job Roles & Responsibilities AI/ML Model & Solution Development: Design and develop advanced AI/ML models using deep learning, reinforcement learning, supervised, and unsupervised learning techniques to solve business-critical problems in real-time. Build cutting-edge solutions across computer vision, NLP, predictive analytics, and recommender systems leveraging frameworks such as TensorFlow, PyTorch, Keras, and scikit-learn. Lead the architecture and implementation of AI-powered systems that scale seamlessly, ensuring high performance, security, and reliability across large datasets and cloud environments. Integrate AI models into production systems with high efficiency, ensuring they are robust, accurate, and scalable. Integrate agents into cloud operations for autonomous monitoring, scaling, and remediation Develop deep learning, reinforcement learning, NLP, computer vision, predictive analytics, and recommender systems. Optimize AI models using hyperparameter tuning, ensembling, transfer learning, and A/B testing. Integrate AI models into production systems ensuring robustness, scalability, and high performance Design and develop autonomous AI agents capable of: Goal-based reasoning Multi-step decision-making Tool/API orchestration Deploy and manage GenAI/LLM models using AWS Bedrock, Azure OpenAI, HuggingFace, LangChain. Implement agent memory, context handling, feedback loops, and multi-agent collaboration. Integrate agents into healthcare cloud operations for autonomous monitoring, scaling, remediation, and workflow automation, including RCM processes like claims processing, denial management, and patient billing. Develop AI/ML models for: Natural Language Processing (NLP): text understanding, claims analysis, clinical notes summarization, chatbots for patient interaction Computer Vision: document scanning, medical imaging analysis Predictive analytics & recommender systems: patient care, claim prediction, revenue forecasting Reinforcement learning & deep learning Optimize AI models using hyperparameter tuning, model ensembling, transfer learning, and A/B testing. Integrate AI models into production systems ensuring robustness, scalability, and high performance, specifically tailored for healthcare and RCM use cases. Data Engineering & Intelligence Pipelines: Build real-time and batch data pipelines using Spark, Hadoop, EMR, Redshift, BigQuery, Databricks, Kafka, AWS Glue. Preprocess, clean, and transform structured and unstructured healthcare datasets, including patient records, claims data, EHRs, and billing data, for AI/ML model training and inference. Enable continuous learning, reinforcement loops, and real-time feedback for AI agents and LLMs. Cloud, API & AgentOps Integration: Expose AI models and agents via secure REST APIs for integration with .NET Core applications and cloud services. Implement AgentOps/MLOps practices: monitoring, versioning, rollout, rollback, and auditing of AI agents and models. Deploy models and agents on cloud-native platforms (AWS SageMaker, Azure ML, GCP AI Platform) using Docker, Kubernetes, and serverless architectures. Intelligent Automation & RPA Enablement: Integrate AI agents with RPA platforms (UiPath, Automation Anywhere) to automate operational workflows, including claims processing, payment posting, and denial resolution. Enable hybrid automation where AI agents coordinate with human approvals as required. Orchestrate bots, scripts, and cloud actions using AI-driven orchestration frameworks. Security, Compliance & Responsible AI: Implement guardrails to ensure safe, compliant, and explainable AI behavior. Ensure compliance with HIPAA, GDPR, SOC 2, and internal AI governance policies. Monitor, audit, and refine agent decisions to prevent model drift, unsafe behavior, or bias, particularly for healthcare data and RCM operations. Research Produ