Senior MLOps Engineer

Systems Limited; Lahore; On-site; PKR 2,400,000 - 4,200,000; Full time

Job summary

Systems Limited is seeking a seasoned MLOps Engineer to operationalize, scale, and govern ML/LLM pipelines on Azure. You will own data ingestion, document classification, OCR extraction, embeddings, and vector search while ensuring security and governance across the stack.

The role requires strong Azure expertise, Python for orchestration, and hands-on CI/CD for ML systems. Responsibilities include RAG pipelines, monitoring, cost optimization, and collaboration with data and backend teams.

Qualifications

• 6+ years of experience in MLOps, ML Engineering, or Platform Engineering.
• Strong expertise in the Microsoft Azure ecosystem.
• Proficiency in Python (pipelines, orchestration, APIs).
• Hands-on experience with CI/CD for ML systems.
• Containerization with Docker and Kubernetes.

Responsibilities

• Operationalize end-to-end ML/LLM pipelines across ingestion, classification, extraction, enrichment, and retrieval.
• Deploy, monitor, and optimize RAG pipelines using Azure OpenAI Service and LangChain.
• Implement CI/CD pipelines for prompt/config changes and data schema evolution.
• Ensure data quality through ingestion, validation, and metadata consistency.
• Build monitoring for pipeline failures, latency, token usage, and costs with Azure Monitor and Logs.
• Enforce security, RBAC, private endpoints, and data residency compliance.
• Optimize LLM usage, embedding storage, and retrieval latency; implement autoscaling.
• Collaborate with Data Engineers, AI Engineers, and Backend teams to enable reusable MLOps templates.

Skills

MLOps

Azure

Python

CI/CD for ML

Docker

Kubernetes

Tools

Azure OpenAI Service

LangChain

Azure Data Factory

Azure ML

Job description

We are seeking a highly skilled MLOps Engineer to operationalize, scale, and govern ML/LLM pipelines across this ecosystem. This role is critical in ensuring the reliability, reproducibility, security, and performance of AI workloads powered by Microsoft Azure and Azure OpenAI Service.

Responsibilities

1. ML/LLM Pipeline Operationalization

• Productionize end-to-end pipelines across:
• Data ingestion (Graph API, Azure Data Factory)
• Document classification (Azure Document Intelligence, LLM-based classifiers)
• Data extraction (OCR + LLM parsing)
• Data enrichment (embeddings, metadata tagging)
• Retrieval (vector and hybrid search)
• Build scalable workflows using:
• Azure Data Factory
• Azure Functions

2. LLMOps & RAG System Management

• Deploy, monitor, and optimize RAG pipelines using:
• Azure OpenAI Service
• LangChain
• Optimize vector search using:
• Azure AI Search

3. Lifecycle Management

• Implement CI/CD pipelines for:
• Prompt and configuration changes
• Data schema evolution
• Work with:
• Azure Machine Learning
• GitHub Actions / Azure DevOps

4. Data & Feature Pipeline Reliability

• Ensure high-quality data ingestion from:
• SharePoint, APIs, batch uploads
• Manage:
• Schema drift
• Data validation
• Metadata consistency
• Work with storage solutions:
• Azure Blob Storage
• Azure Cosmos DB

5. Monitoring, Observability & Quality

• Build monitoring systems for:
• Pipeline failures
• Latency (retrieval & generation)
• Token usage and cost tracking
• Data and embedding drift
• Utilize:
• Azure Monitor
• Log Analytics
• Application Insights

6. Security, Compliance & Governance

• Enforce enterprise-grade controls:
• RBAC, private endpoints, VNet isolation
• Encryption via Azure Key Vault
• Ensure compliance with:
• SOC2, data residency, audit logging
• Implement safe AI practices:
• Guardrails for LLM outputs
• PII handling and redaction

7. Performance & Cost Optimization

• Optimize:
• LLM usage (prompt efficiency, caching)
• Embedding storage and retrieval latency
• Implement:
• Autoscaling strategies
• Cost monitoring dashboards
• Tune:
• Chunk sizes, retrieval depth, hybrid search weights

8. Collaboration & Enablement

• Collaborate with:
• Data Engineers (ingestion pipelines)
• AI Engineers (models, prompting)
• Backend teams (API layer)
• Enable teams through:
• Reusable MLOps templates
• Documentation and best practices

Qualifications

• 6+ years of experience in MLOps, ML Engineering, or Platform Engineering
• Strong expertise in the Microsoft Azure ecosystem
• Proficiency in Python (pipelines, orchestration, APIs)
• Hands-on experience with:
• CI/CD for ML systems
• Containerization (Docker, Kubernetes)

Preferred (LLM / RAG Experience)

• Experience with:
• Azure OpenAI Service or similar platforms
• LangChain
• Strong understanding of:
• Embeddings and vector databases
• Prompt engineering lifecycle
• RAG evaluation techniques

Location

• LHR
• ISB
• KHI