Senior AI/ML Engineer - 100% remote

Role Overview

You will be a “day-one” engineer owning the production lifecycle of the client’s AI initiatives within their AI Transformation Office. The work focuses on building automated infrastructure that connects legacy insurance data systems to modern multi-cloud AI services.

Responsibilities

• Multi-cloud pipeline execution: Build and maintain automated CI/CD and Continuous Training pipelines across AWS (SageMaker/Bedrock) and Azure (AI Studio).
• LLMOps framework implementation: Deliver RAG infrastructure, including vector database management (OpenSearch, Pinecone, or Azure AI Search) and semantic index optimization.
• Legacy data connectivity: Create secure ingestion/data movement pipelines from Mainframes, SQL Server, and on-prem DBs into cloud-native MLOps workflows.
• Automated model evaluation: Implement repeatable LLM evaluation (e.g., LLM-as-a-judge, ROUGE, METEOR) and traditional ML validation before deployment.
• Observability & monitoring: Monitor model drift, hallucinations, latency, and token consumption for quality and cost control.
• Infrastructure as Code: Manage AI infrastructure using Terraform or CloudFormation with reproducibility, security, and Privacy by Design.
• Advanced analytics integration: Partner with teams using tools like Palantir, Databricks, or Snowflake to maintain high-fidelity data flows to production models.
• IT & security enablement: Coordinate with IT/Security on IAM roles, VPC peering, and firewall/network access.
• Scalable inference engineering: Optimize serving endpoints for high throughput and low latency using Docker/Kubernetes and serverless architectures where appropriate.
• Prompt & model versioning: Implement audit-ready version control for prompts, model weights, and data snapshots with rollback.
• Data science engineering support: Automate feature stores, feature engineering pipelines, and productionize notebooks into microservices.
• Security & compliance hardening: Add automated scanning/guardrails to prevent prompt injection and data leakage (e.g., Bedrock Guardrails or Azure Content Safety).

Requirements

• 2+ years of production engineering experience in MLOps and/or LLMOps.
• Python, SQL, and PySpark.
• Containerization and orchestration: Docker, Kubernetes, and one or more of Airflow, Kubeflow, or Step Functions.

Nice to Have

• Strong hands-on experience across both AWS and Azure, including ability to configure Bedrock and Azure OpenAI with private networking and endpoint security.
• Experience with LLM evaluation/observability tools such as LangSmith, Arize Phoenix, or WhyLabs.