Staff Engineer

Roles & Responsibilities:

• Chunking (primarily focused on VectorDB storage).
• Document Parsing and OCR
• Document Parsing with VLMs (Vision Language Models)
• Function Calling with LLMs
• Retrieval Augmented Generation
• Traditional Search (BM25, NER based parsers, Keyword based search index)
• Semantic Search (Embeddings, Embedding models)
• Fine Tuning using LoRA
• Merging multiple LoRA adapters using MergeKit
• Quantising LLMs
• Prompt Engineering techniques
• 4+ years with Azure (ML pipeline components), Azure Databricks, Azure DevOps.
• Proven experience of design and deployment of end-to-end ML pipelines.
• Experience with building infrastructure for classic DS models and/or LLM/SLMs.
• 4+ years with orchestration (e.g., Kubeflow, Airflow, Azure Data Factory) and CI/CD for ML.
• Experience deploying containerized ML solutions (e.g. Docker/Kubernetes).
• Knowledge of model and data versioning (e.g. MLflow, DVC).
• Knowledge of MLSecOps (security in the context of MLOps)
• Experience with Infrastructure as a Code (e.g., Terraform, CloudFormation).
• Knowledge of MLOps for LLM/SLM
• Experience in ML system/architecture design (load balancing, caching, failover).
• Knowledge in building scalable, resilient ML architectures.
• Cross-team collaboration experience (data science, engineering, DevOps).
• Experience with monitoring/logging for production models (e.g. Prometheus, Grafana, ELK stack).

Qualifications

• 7+ years in ML Ops / DevOps / data engineering
• Strong Python skills, plus experience with MLflow, Kubeflow, or Airflow
• Hands-on with Docker, Kubernetes, and cloud platforms
• Knowledge of data and model versioning tools (e.g., DVC, MLflow)