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

Staff Engineer