MLOps + LLMOps Engineer

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

- 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.
- Robust 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.

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 MLOps + LLMOps Engineer (Maharashtra)
🏢 Credence Resource Management
📍 Maharashtra