Product Development

Position Overview

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.

Job Roles & Responsibilities

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.

Candidate Requirements

• 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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