Senior MLOps Engineer

Systems Limited; Lahore; On-site; PKR 3,000,000 - 5,400,000; Full time

Job summary

Systems Limited is seeking an experienced MLOps Engineer to operationalize, scale, and govern ML/LLM pipelines across the Azure ecosystem. You will ensure reliability, reproducibility, security, and performance of AI workloads powered by Microsoft Azure and Azure OpenAI Service.

Responsibilities include end-to-end pipeline productionization, LLMOps with RAG, lifecycle management, monitoring, and governance. A strong background in Python, CI/CD for ML, and cloud security is required.

Qualifications

• 6+ years of experience in MLOps, ML engineering, or platform engineering.
• Strong expertise in the Microsoft Azure ecosystem.
• Proficiency in Python for pipelines, orchestration, and APIs.
• Hands-on experience with CI/CD for ML systems.

Responsibilities

• Operationalize end-to-end ML/LLM pipelines across the ecosystem.
• Deploy, monitor, and optimize RAG pipelines using LangChain.
• Manage lifecycle changes: prompts, configurations, and data validation.
• Collaborate on GitHub Actions / Azure DevOps workflows.
• Ensure data ingestion from SharePoint, APIs, and batch uploads; monitor schema drift.
• Implement security governance: RBAC, private endpoints, data residency, audit logging.
• Build observability: token usage, cost tracking, data/embedding drift.

Skills

MLOps

Azure

Python

CI/CD for ML

LLM/RAG

Monitoring

Security

Cost optimization

Autoscaling

Tools

LangChain

GitHub Actions

Azure DevOps

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:

• Productionize end-to-end pipelines across:
• Retrieval (vector and hybrid search)
• Build scalable workflows using:

2. LLMOps & RAG System Management

• Deploy, monitor, and optimize RAG pipelines using:
• LangChain
• Optimize vector search using:

3. Lifecycle Management

• Prompt and configuration changes
• Work with:
• GitHub Actions / Azure DevOps
• Ensure high-quality data ingestion from:
• SharePoint, APIs, batch uploads
• Manage:
• Schema drift
• Data validation
• Work with storage solutions:

5. Monitoring, Observability & Quality

• Build monitoring systems for:
• Token usage and cost tracking
• Data and embedding drift
• Application Insights

6. Security, Compliance & Governance

• RBAC, private endpoints, VNet isolation
• 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
• Tune:
• Collaborate with:
• 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

Preferred (LLM / RAG Experience)

• Experience with: