AI Sr. Engineer LLMOps & MLOps

Job Description

Job Description

Description:
AI Sr. Engineer LLMOps & MLOps

Job Description
Role Overview
This is a high-stakes, execution-focused role within the Transformation Office.
We are looking for a "day-one" engineer to own the production lifecycle of our AI initiatives.
Your mission is to build the automated infrastructure that bridges our legacy data systems with modern AWS and Azure AI services.
You will be responsible for the "Ops" of AI: ensuring that LLM applications, RAG pipelines, and traditional ML models are deployable, observable, and scalable in a multi-cloud environment.

Key Responsibilities
Multi-Cloud Pipeline Execution: Build and maintain automated CI/CD and CT (Continuous Training) pipelines across AWS (SageMaker/Bedrock) and Azure (AI Studio).
LLMOps Framework Implementation: Design and execute the infrastructure for Retrieval-Augmented Generation (RAG), including vector database management (OpenSearch, Pinecone, or Azure AI Search) and semantic index optimization.
Legacy Data Connectivity: Build the engineering "pipes" to securely ingest and move data from legacy systems (Mainframes, SQL Server, on-prem DBs) into cloud-native MLOps workflows.
Automated Model Evaluation: Implement systemized frameworks for LLM evaluation (LLM-as-a-judge, ROUGE, METEOR) and traditional ML validation to ensure performance before deployment.
Observability & Monitoring: Deploy real-time monitoring for model drift, hallucination detection, latency, and token consumption to manage both quality and cost.
Infrastructure as Code (IaC): Manage all AI resources using Terraform or CloudFormation, ensuring the cloud posture is reproducible, secure, and follows a "Privacy by Design" mandate.
Advanced Analytics Integration: Partner with teams using platforms like Palantir, Databricks, or Snowflake to ensure a high-fidelity data flow between analytical ontologies and production models.
IT & Security Diplomacy: Work directly with central IT and Security to navigate IAM roles, VPC peering, and firewall configurations, clearing the path for rapid transformation.
Scalable Inference Engineering: Optimize model serving endpoints for high-throughput and low-latency, utilizing containerization (Docker/Kubernetes) and serverless architectures where appropriate.
Prompt & Model Versioning: Establish rigorous version control for prompts (PromptOps), model weights, and data snapshots to ensure 100% auditability and rollback capability.
Data Science Engineering: Support the data science lifecycle by automating feature stores, feature engineering pipelines, and the transition of experimental notebooks into hardened production microservices.
Security & Compliance Hardening: Implement automated scanning and guardrails (e.g., Bedrock Guardrails or Azure Content Safety) to prevent prompt injection and data leakage.