Job Role : Lead MLOps Engineer
Location : : 7+ Years
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., Kubeflow, Airflow, Azure Data Factory) and CI / CD for ML.
- Experience deploying containerized ML solutions (e., Docker / Kubernetes).
- Knowledge of model and data versioning (e., MLflow, DVC).
- Knowledge of MLSecOps (security in the context of MLOps)
- Experience with Infrastructure as a Code (e., 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., Prometheus, Grafana, ELK stack).
Qualifications :
7+ years in ML Ops / DevOps / data engineeringStrong Python skills, plus experience with MLflow, Kubeflow, or AirflowHands-on with Docker, Kubernetes, and cloud platformsKnowledge of data and model versioning tools (e., DVC, MLflow)We offer :
Opportunity to work on bleeding-edge projectsWork with a highly motivated and dedicated teamCompetitive salaryFlexible scheduleBenefits package - medical insurance, sportsCorporate social eventsProfessional development opportunitiesWell-equipped office(ref : hirist.tech)