MLOps, LLMOps Engineer – Mid-Level

Stellenbeschreibung:

Operationalize the complete ML lifecycle—training, evaluation, packaging, deployment, and monitoring—on Databricks.
Implement ML workflows using Bronze → Silver → Gold medallion architecture with Delta Lake.
Establish Unity Catalog model-management patterns for governance, lineage, discovery, and access control.
Develop reusable templates for ML/LLM jobs, workflows, and deployment processes.
Create and maintain cluster policies aligned with enterprise platform guardrails.
Productionize ML and GenAI models for damage prevention, asset integrity, land management, and stakeholder engagement use cases.
Design, build, and maintain production‑grade LLM and RAG pipelines.
Implement vector search and retrieval architectures using technologies such as Databricks Vector Search.
Deploy and manage model‑serving and inference endpoints; optimize performance, scalability, reliability, and cost.
Implement batch, streaming, and online inference patterns and establish service‑level expectations and SLAs.
Integrate data contracts, quality gates, PII controls, data residency policy‑as‑code, and end‑to‑end lineage into the ML lifecycle.
Use Databricks Asset Bundles and GitHub Actions to version, test, and promote ML/LLM assets across DEV, QA, and PROD.
Build automated unit, integration, regression, data‑quality, and model‑quality test suites with deployment gates.
Instrument pipelines and services for SLOs, monitoring, alerting, Jira ticket creation, model/data/feature drift detection, and LLM observability.
Develop runbooks, troubleshooting procedures, operational documentation, disaster‑recovery procedures, and participate in on‑call rotations and DR testing.
Enforce cost and ownership tags, support showback/chargeback reporting, monitor AI infrastructure and inference costs, and identify optimization opportunities.
Collaborate with Data Science, Data Engineering, Platform, Product, Security, and domain teams.

Requirements

3–5 years of experience in MLOps, LLMOps, ML Engineering, Data Engineering, or platform‑focused ML engineering.
Hands‑on experience with Databricks Jobs and Workflows, Delta Lake, Unity Catalog, and Databricks SQL Warehouses.
Experience building and maintaining CI/CD pipelines for data and ML workloads using GitHub Actions and Databricks Asset Bundles (DABs).
Experience with DEV → QA → PROD environment promotion and parameterized deployments.
Experience with secure secrets management using Azure Key Vault, AWS KMS/Secrets Manager, or equivalent technologies.
Strong understanding of data contracts, schema governance, and automated data/feature validation.
Experience with Great Expectations‑style validation frameworks or equivalent rule‑based data‑quality solutions.
Experience building observable production pipelines with metrics, dashboards, alerting, and SLO monitoring.
Practical experience with RBAC/ABAC, Unity Catalog security, PII detection and obfuscation, private networking, data‑access controls, and policy‑as‑code for data residency.
Strong proficiency in Python and SQL.
Working knowledge of distributed computing and job orchestration within Databricks/Spark environments.
Ability to troubleshoot production ML/data workloads and participate in operational support and incident resolution.
Preferred: hands‑on experience with LLM/GenAI workflows, prompt engineering, RAG, LLM evaluation, AI safety and guardrails, retrieval/response‑quality evaluation, latency optimization, and token/API‑cost optimization.
Preferred: experience with geospatial data and analytics, including PostGIS, spatial joins, spatial indexing and tiling, coordinate systems and projections, and GIS‑based feature engineering.
Preferred: experience integrating Power BI with Databricks SQL Warehouses and semantic layers.
Preferred: practical knowledge of FinOps, including resource tagging, budget management, cost monitoring, showback/chargeback, and cost anomaly detection.
Preferred: knowledge of Databricks disaster‑recovery patterns, including Delta Lake Deep Clone, Delta Sharing, cross‑region recovery, tiered RTO/RPO strategies, and DR testing.
Preferred: hands‑on experience with Microsoft Azure and AWS.
Preferred: understanding of cloud‑native security patterns, including Private Link, VPC/VNet connectivity and peering, egress restrictions, KMS, AWS Secrets Manager, Azure Key Vault, and data‑plane isolation.

Core Competencies

Demonstrates expertise in operationalizing the ML lifecycle, including training, evaluation, deployment, and monitoring, with a strong focus on Databricks and Delta Lake. Proficient in implementing governance and security measures for ML models, as well as building observable production pipelines and CI/CD workflows.