Senior ML Engineer
Lorven Technologies Inc
Austin, TX, United States
2 days ago
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Role details
Contract type
Contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
8 years minimum
Working hours
Regular working hours
Job source
Tech stack
Artificial Intelligence
Audit Trail
Continuous Integration
Graph Database
Information Extraction
Python (Programming Language)
Machine Learning
Language Modeling
Natural Language Processing
NoSQL
Search Technologies
Software Engineering
+17 more
SQL Databases
Google Cloud
Enterprise Software Applications
Feature Engineering
Retrieval-Augmented Generation
Large Language Models
Model Validation
Generative AI
Containerization
Information Technology
Machine Learning Operations
Drift Detection
Model Explainability
Restful APIs
Intelligent Document Processing
Software Version Control
Data Pipelines
Requirements
- Bachelor’s or advanced degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
- 8+ years of experience in software engineering, data science, or machine learning, with production AI/ML delivery experience.
- Strong hands-on experience in Python, AI/ML, model development, training, validation, and optimization.
- Experience with Generative AI, NLP, LLMs, embeddings, RAG, and document intelligence.
- Hands-on experience with the Google Cloud Platform (Google Cloud Platform) ecosystem and enterprise/on-premises environments.
- Experience with Vector Databases, vector search, and Knowledge Graphs.
- Knowledge of Small Language Models (SLMs), Vision-Language Models (VLMs), and OCR/Intelligent Document Processing is a plus.
- Strong understanding of statistics, probability, feature engineering, model evaluation, and hyperparameter tuning.
- Experience with MLOps, model versioning, CI/CD, containerization, monitoring, and drift detection.
- Experience developing secure REST APIs and integrating AI/ML solutions with enterprise applications.
- Knowledge of model explainability, bias assessment, governance, data privacy, and auditability.
- Experience with SQL/NoSQL databases, data pipelines, and production model deployment.
- Ability to mentor engineers, conduct design reviews, and prepare technical documentation.
- Strong analytical, problem-solving, and communication skills.
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