We are seeking a Senior MLOps & LLM Ops Engineer to lead the design, deployment, and management of AI/ML and LLM pipelines, Agentic AI frameworks, and autonomous agent workflows in a US Healthcare Revenue Cycle Management (RCM) platform. This role will integrate cloud infrastructure, microservices, GenAI/LLMs, RPA, Big Data, and autonomous agents to ensure scalable, compliant, and production-ready AI systems.
You will own CI/CD, data pipelines, model deployment, LLM operations, and multi-agent orchestration, enabling real-time decision-making across Claims, Prior Authorization, Scheduling, Coding, Collections, and EDI modules.
MLOps & LLM Ops Leadership:
- Design, implement, and manage end-to-end MLOps pipelines, including training, validation, deployment, and monitoring of AI/ML models and LLMs.
- Implement LLM Ops best practices: model versioning, prompt management, safe rollout/rollback, and inference monitoring.
- Optimize pipelines for real-time and batch inference, serving Agentic AI workflows, autonomous agents, and RPA systems.
- Enable continuous learning and feedback loops for LLMs and AI agents using operational data.
- Collaborate with data science teams to productionize GenAI models, ensuring robust model governance and auditability.
Cloud, CI/CD & Infrastructure:
- Design, deploy, and maintain cloud infrastructure (AWS, Azure, GCP) for AI/ML and LLM workloads.
- Implement Infrastructure-as-Code (Terraform, CloudFormation) and containerized deployments (Docker, Kubernetes).
- Build CI/CD pipelines for AI/ML models, LLMs, Agentic AI, autonomous agents, and bots, including automated testing, validation, and rollback.
- Monitor resource utilization, latency, and throughput to ensure high-performance inference and autonomous operations.
- Implement multi-agent orchestration enabling AI agents and bots to collaborate on complex workflows.
Data Engineering & Big Data Integration:
- Design data pipelines for training, evaluation, and inference using Snowflake, Spark, Hadoop, EMR, or Redshift.
- Ensure data quality, integrity, and compliance for PHI-sensitive healthcare data (HIPAA).
- Enable real-time telemetry, logs, and metrics to feed LLMs and autonomous agents for decision-making.
- Collaborate with RPA teams to orchestrate agent-driven automation using operational insights.
Agentic AI & Autonomous Systems:
- Deploy and manage Agentic AI frameworks using GenAI/LLMs for autonomous decision-making and goal-driven workflows.
- Enable autonomous monitoring, remediation, and optimization of cloud and application systems.
- Implement agent memory, context handling, and multi-step reasoning to support intelligent workflow automation.
- Integrate AI agents with RPA platforms to execute operational workflows autonomously or with human-in-the-loop approvals.
Monitoring, Compliance & Governance:
- Implement AgentOps/MLOps practices, including agent/LLM behavior monitoring, versioning, and audit trails.
- Ensure compliance with HIPAA, SOC 2, GDPR, and internal policies for AI models and agents.
- Design guardrails for safe, explainable, and accountable AI/LLM/autonomous workflows.
- Enable role-based access, secrets management, and secure API integration for AI agents and LLM inference.
Mentorship & Collaboration:
- Mentor junior MLOps, LLM Ops, and DevOps engineers on AI pipeline best practices.
- Collaborate across data science, cloud, product, RPA, and QA teams to accelerate AI deployment.
- Lead architecture reviews, pipeline optimization, and adoption of next-gen AI/LLM technologies.
- Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or related field.
- 6–12+ years in MLOps, DevOps, LLM Ops, or AI infrastructure engineering.
- Proven experience with cloud AI deployments (AWS, Azure, GCP) and containerized ML systems (Docker, Kubernetes).
- Hands-on experience with LLM deployment, prompt management, GenAI pipelines, and autonomous agent orchestration.
- Strong background in Big Data, ETL/ELT pipelines, and RPA integrations.
- Knowledge of HIPAA, SOC 2, and healthcare data compliance.
- Experience in microservices, API integrations, and event-driven architectures.
Technical Expertise:
- Cloud & Infrastructure: AWS, Azure, GCP, Terraform, CloudFormation, VPC, IAM, Security Groups
- CI/CD & MLOps/LLM Ops: Jenkins, GitHub Actions, GitLab CI, ArgoCD, MLflow, Kubeflow, Airflow, Ansible, Docker, Kubernetes
- AI/GenAI/Agentic AI: LLMs, GenAI models, autonomous agents, multi-agent workflows, context/memory handling, AgentOps
- Data & Analytics: Snowflake, Spark, Hadoop, EMR, Redshift, ETL/ELT pipelines, real-time streaming
- RPA & Automation: UiPath, Automation Anywhere, agent-to-bot orchestration
- Microservices & Integration: REST APIs, .NET Core microservices, Kafka, RabbitMQ, event-driven architectures
- Compliance & Security: HIPAA, SOC 2, GDPR, role-based access, audit trails, secrets management
Skillset:
- Ability to design end-to-end AI/LLM pipelines with autonomous agent integration.
- Strong analytical and problem-solving skills for distributed, high-throughput AI systems.
- Experience mentoring engineers and collaborating with cross-functional teams.
- Excellent understanding of AI safety, explainability, and governance.
- High ownership, attention to detail, and strategic mindset for scalable AI infrastructure.
Strategic Impact:
- Enable autonomous AI/LLM-driven operations across RCM workflows.
- Ensure scalable, compliant, and production-ready AI pipelines.
- Accelerate analytics, GenAI features, and agent-driven automation.
- Reduce deployment failures, operational overhead, and ensure audit readiness.
- Position the organization as a leader in autonomous, agentic AI platforms in healthcare.
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