ThreatXIntel
Website:
threatxintel.com
Job details:
Company Description
ThreatXIntel is a growing Cybersecurity, IT Staffing, and Consulting company delivering end-to-end technology and security solutions. Our services include cloud security, web and mobile application security testing, DevSecOps, vulnerability assessments, IT consulting, and professional staffing services.
We support global corporate clients by hiring and deploying skilled professionals across IT and cybersecurity domains while helping organizations strengthen security, optimize operations, and scale efficiently. ThreatXIntel is committed to enabling business growth through secure, reliable, and high-quality technology solutions.
Role Overview
We are seeking an experienced Databricks ML/LLM Engineer with strong expertise in Machine Learning, Large Language Models (LLMs), and Databricks Lakehouse platform.
The consultant will design, develop, and deploy scalable ML and Generative AI solutions using Databricks for enterprise data and AI workloads.
Key Responsibilities
- Develop and deploy Machine Learning models using Databricks.
- Build and optimize LLM-based applications including Retrieval-Augmented Generation (RAG) pipelines.
- Work with Databricks Lakehouse architecture for data engineering and ML workflows.
- Develop ML pipelines using Databricks MLflow.
- Implement data preprocessing, feature engineering, and model training workflows.
- Integrate LLMs with enterprise datasets and APIs.
- Optimize model performance, inference latency, and scalability.
- Collaborate with data engineers, analysts, and product teams.
- Deploy AI solutions using cloud platforms (Azure/AWS/GCP).
- Maintain model monitoring, experiment tracking, and version control.
Required Technical Skills
Databricks & Data Platform
- Databricks workspace development
- Databricks Lakehouse architecture
- Delta Lake
- Spark / PySpark
Machine Learning & AI
- ML model development and deployment
- LLM integration and prompt engineering
- RAG pipelines and vector search
- Model evaluation and optimization
Programming
MLOps
- MLflow experiment tracking
- CI/CD for ML models
- Model monitoring and lifecycle management
Cloud Platforms
- Azure Databricks / AWS / GCP experience
Preferred Skills
- Vector databases (FAISS, Pinecone, pgvector)
- LangChain or LlamaIndex frameworks
- Conversational AI applications
- Production AI system deployment
Click on Apply to know more.