MLOps Engineer

We are building an enterprise-scale, Azure-native

Document Intelligence and Retrieval-Augmented Generation (RAG)

platform designed to ingest, classify, extract, enrich, and serve knowledge from large volumes of structured and unstructured data (SharePoint, PDFs, emails, and more).

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