MLOps / LLMOps Engineer (Mid-Level)

About This Opportunity

Our talent team is representing a confidential SaaS organization serving enterprise clients in critical infrastructure sectors across North America. We are seeking a Mid-Level MLOps / LLMOps Engineer to join their AI/ML insights team remotely from India.

This role focuses on building production-grade machine learning and LLM infrastructure on a governed, multi-cloud lakehouse platform. You'll establish the engineering patterns that enable data science teams to move from experimentation to reliable production AI at scale.

Responsibilities

Build the ML/LLM Platform:

• 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, and access control
• Develop reusable templates for ML/LLM jobs, workflows, and deployment processes
• Create cluster policies aligned with enterprise guardrails including private networking, resource tagging, LTS runtimes, and secure secrets management

Productionize ML & LLM Features:

• Partner with data science and product teams to deploy models supporting risk scoring, anomaly detection, predictive maintenance, geospatial enrichment, and NER
• Design and maintain production-grade LLM and RAG pipelines
• Implement vector search and retrieval architectures using Databricks Vector Search
• Deploy and manage model-serving and inference endpoints
• Optimize inference workloads through quantization, distillation, caching, and batching techniques
• Implement batch, streaming, and online inference patterns based on latency requirements

Engineer Reliability, Security & Compliance:

• Integrate data contracts and quality gates into ML/LLM pipelines
• Implement automated validation for schema drift, null thresholds, duplicates, referential integrity, and completeness
• Implement PII detection, classification, masking, and obfuscation
• Enforce data residency requirements through policy-as-code
• Maintain end-to-end lineage from source tables through features, models, serving endpoints, to applications
• Support security, compliance, audit, and access-review requirements

Automate CI/CD & Testing:

• Use Databricks Asset Bundles and GitHub Actions to version, test, and promote ML/LLM assets across DEV → QA → PROD
• Version and manage jobs, notebooks, model artifacts, cluster policies, configuration, and permissions
• Build automated unit, integration, regression, and data-quality test suites
• Implement model-quality validation and deployment gates
• Validate business KPIs against the Unity Catalog semantic layer before production publication

Observability & Production Operations:

• Instrument pipelines to support SLOs including ≥99.5% P1 pipeline success, ≤5 minute MTTD, ≤60 minute MTTR
• Implement proactive monitoring, alerting, and operational dashboards
• Monitor for model performance degradation, data drift, concept drift, and feature-quality issues
• Establish LLM-specific observability for hallucination rates, response quality, latency, token consumption, and inference costs
• Develop production runbooks and disaster-recovery procedures with tiered RTO/RPO objectives
• Participate in on-call rotations for critical ML/LLM services

FinOps & Cost Management:

• Enforce mandatory cost and ownership tags across ML/LLM infrastructure
• Support showback and chargeback reporting
• Monitor compute, storage, model-serving, and LLM/API costs
• Identify optimization opportunities without compromising service quality or SLOs
• Detect and investigate abnormal cost increases or inefficient workloads

What We're Looking For

Required Qualifications:

• 3–5 years of experience in MLOps, LLMOps, ML Engineering, Data Engineering, or platform-focused ML engineering
• Hands-on experience with Databricks including Jobs/Workflows, Delta Lake, Unity Catalog, and SQL Warehouses
• Proven experience building CI/CD pipelines for data and ML workloads using GitHub Actions and Databricks Asset Bundles
• Experience with environment promotion across DEV → QA → PROD with parameterized deployments
• Secure secrets management using Azure Key Vault, AWS KMS/Secrets Manager, or equivalent
• Strong understanding of data contracts, schema governance, and automated data/feature validation
• Experience implementing Great Expectations-style validation frameworks or equivalent
• Experience building observable production pipelines with metrics, dashboards, alerting, and SLO monitoring
• Strong security-first mindset with practical experience in RBAC/ABAC, Unity Catalog security, PII detection/obfuscation, and data-access controls
• 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 incident resolution

Preferred Qualifications:

• Hands-on experience with LLM/GenAI workflows including prompt engineering, RAG, LLM evaluation frameworks, AI safety/guardrails, and token/API-cost optimization
• Experience with geospatial data and analytics including PostGIS, spatial joins, indexing, coordinate systems, and GIS-based feature engineering
• Experience integrating Power BI with Databricks SQL Warehouses and semantic layers including dataset refresh SLAs, query concurrency, and Row-Level Security
• Practical knowledge of FinOps including resource tagging, budget management, cost monitoring, and showback/chargeback
• Knowledge of Databricks disaster-recovery patterns including Delta Lake Deep Clone, Delta Sharing, cross-region recovery, and tiered RTO/RPO strategies
• Hands-on experience with Microsoft Azure and AWS for ML and data workloads
• Understanding of cloud-native security patterns including Private Link, VPC/VNet connectivity, egress restrictions, KMS, and data-plane isolation

Nice-to-Have:

• Experience deploying models supporting risk scoring, asset integrity, anomaly detection, or predictive maintenance
• Experience building CI/CD workflows with automated data contracts and quality gates for infrastructure or maintenance-related models
• Familiarity with monitoring data/concept drift, model performance, SLOs, pipeline health, and incident management
• Understanding of data residency, security, privacy, compliance, and disaster-recovery requirements for infrastructure data

How We Work with You

Candidates apply directly through Get A Job.ai. Our recruiting team will screen qualified applicants and coordinate interviews with our client. We handle all communication and scheduling throughout the process. Please do not contact the client organization directly—all applications must come through Get A Job.ai to be considered.

Pay

Compensation details will be discussed with qualified candidates during the screening process. Pay is competitive and based on experience and qualifications.

Additional Benefits (through the client):

• Comprehensive medical, dental, and vision insurance
• Generous paid time off and company-paid holidays
• Flexible remote work arrangements
• On-call compensation for eligible shifts

Get A Job.ai is an equal opportunity recruiter. We welcome applications from candidates of all backgrounds and do not discriminate based on race, color, religion, sex, national origin, age, disability, or any other protected characteristic.