FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Machine Learning Engineer – Contract
Tech Holding. Design, train, and deploy ML models for text classification, entity extraction, clustering, and narrative and topic detection .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing, training, and deploying machine learning models, particularly in natural language processing and large language models. Proficient in building scalable ML services and evaluation frameworks to ensure model quality and performance in production environments.
Highest-signal resume keywords
Machine Learning Model DeploymentNatural Language Processing (NLP)Python ProgrammingExperience with LLMsModel Evaluation and Monitoring
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningNatural Language ProcessingModel EvaluationPrompt EngineeringEntity ExtractionClusteringText ClassificationSemantic SearchData IngestionFeature Engineering
Soft Skills
CollaborationCommunicationProblem-Solving
Tools & Technologies
PyTorchTensorFlowHugging Face TransformersAWSGCPAzureDockerCI/CDPgvectorPinecone
Industry Keywords
Risk IntelligenceEmerging ThreatsMultilingual Text DataRetrieval-Augmented Generation (RAG)Bias and Drift Monitoring
Tech Stack
Tools & technologiesAWSAzureDockerGoogle Cloud PlatformPythonPyTorchTensorflow
About the role
Key responsibilities & impact- Design, train, and deploy ML models for text classification, entity extraction, clustering, and narrative and topic detection
- Build and improve LLM-powered features, including prompt design, retrieval-augmented generation (RAG), and agentic workflows
- Develop embedding and semantic-search pipelines to group related content and surface emerging threats
- Productionize models as scalable, monitored services with clear latency, cost, and accuracy targets
- Build evaluation frameworks and datasets to measure model quality, bias, and drift over time
- Work with data engineering to improve ingestion, labeling, and feature pipelines for high-volume, noisy, multilingual text data
- Monitor and retrain models in production, and debug quality issues end to end
- Join daily standups and technical discussions with the US-based client team
- Build an AI-powered risk intelligence platform that helps enterprises detect, assess, and respond to emerging online threats
Requirements
What you’ll need- 4+ years of professional experience building and deploying machine learning models in production
- Strong Python skills and hands-on experience with PyTorch, TensorFlow, or Hugging Face Transformers
- Practical NLP experience, such as classification, named entity recognition (NER), topic modeling, or semantic similarity on real-world text data
- Hands-on experience building with LLMs, including prompt engineering and RAG
- Experience with embeddings and vector search, such as pgvector, Pinecone, OpenSearch, or FAISS
- Experience deploying and operating ML services on AWS, GCP, or Azure, using Docker and CI/CD
- Solid grasp of model evaluation, including metrics, test sets, error analysis, and production monitoring
Benefits
Comp & perks- Equal Opportunity Employer committed to fostering a diverse and inclusive workplace
- Application process accommodation available
- Remote work from Mexico