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Job Description

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)

More Info

Job Type:
Function:
Employment Type:
Open to candidates from:
Indian

Job ID: 119876779

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