Ekfrazo Technologies - Natural Language Processing Engineer - Python

Role : NLP Engineer

Experience : 4-8 Years

Location : Bagmane Tech Park, Bangalore

Mode : Hybrid

Availability : Immediate joiners or candidates serving notice period with LWD by end of this month

About the Role :

We are seeking an experienced NLP Engineer to design, build, and deploy advanced Natural Language Processing models tailored to logistics data. The role involves developing solutions for text extraction, classification, anomaly detection, and intelligent routing while leveraging Azure cloud services for scalable deployment.

Key Responsibilities :

- Develop, fine-tune, and deploy NLP models for text-based logistics data using Hugging Face, spaCy, or Azure Cognitive Services.

- Implement models for SKU classification, anomaly detection, and automated routing in logistics workflows.

- Build and optimize data ingestion and preprocessing pipelines integrating with APIs, warehouse management systems, and image recognition outputs.

- Deploy and manage ML models on Azure Machine Learning and Azure Kubernetes Service, ensuring high availability and minimal latency.

- Establish monitoring pipelines for NLP models with Application Insights and other performance-tracking tools.

- Continuously improve models with retraining workflows using feedback loops and performance analytics.

- Collaborate with cross-functional teams (data engineers, product teams, and operations) to ensure alignment with business goals.

- Document NLP models, workflows, and performance reports for knowledge sharing and scalability.

Required Skillsets :

- Strong expertise in NLP frameworks : Hugging Face Transformers, spaCy, NLTK, or similar.

- Proficiency in Python and libraries like PyTorch, TensorFlow, Scikit-learn.

- Hands-on experience with Azure AI services, Azure Machine Learning, and Kubernetes (AKS).

- Knowledge of data engineering workflows including preprocessing, ETL, and API integration.

- Experience in model deployment and serving using cloud-native architectures.

- Familiarity with monitoring and logging tools (Application Insights, Prometheus, Grafana).

- Strong problem-solving and analytical skills for anomaly detection and logistics-specific challenges.

- Excellent understanding of deep learning concepts like embeddings, transformers, and sequence models.