Machine Learning Engineer – Contract

Tech Holding

. Design, train, and deploy ML models for text classification, entity extraction, clustering, and narrative and topic detection .

Posted 10/13/2026contractRemote • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core 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 evaluating model performance in production environments.

Highest-signal resume keywords

Machine Learning Model DeploymentNatural Language Processing (NLP)Python ProgrammingExperience with LLMsModel Evaluation and Monitoring

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningText ClassificationEntity ExtractionClusteringPrompt EngineeringSemantic SearchModel Evaluation MetricsError AnalysisMultilingual NLPData Processing

Soft Skills

Strong Communication SkillsCross-Functional Collaboration

Tools & Technologies

PyTorchTensorFlowHugging Face TransformersAWSGCPAzureDockerCI/CDMLflowKafka

Industry Keywords

Risk IntelligenceDisinformation ResearchThreat IntelligenceAgentic AI SystemsSocial Media Data

Tech Stack

Tools & technologies

AirflowAWSAzureDockerGoogle Cloud PlatformKafkaPythonPyTorchSparkTensorflow

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
Document approaches and share trade-offs clearly with technical and non-technical stakeholders
Work closely with data engineers, backend engineers, and product stakeholders as an individual contributor
Help build an AI-powered risk intelligence platform that enables enterprises to detect, assess, and respond to disinformation, reputational attacks, and security risks

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 (OpenAI, Anthropic, or open-source models), 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
Strong communication skills and comfort working in a remote, cross-functional team
Applicants must be authorized to work for any employer in the U.S.; visa sponsorship is unavailable
Nice-to-have: experience with trust and safety, threat intelligence, disinformation research, or social media data
Nice-to-have: experience building agentic AI systems or tool-using LLM workflows, such as LangChain, LlamaIndex, or custom frameworks
Nice-to-have: experience with multilingual NLP
Nice-to-have: familiarity with MLOps tooling such as MLflow, SageMaker, Vertex AI, or Weights & Biases
Nice-to-have: experience with streaming or large-scale data processing, such as Kafka, Spark, or Airflow
Nice-to-have: experience fine-tuning LLMs with LoRA or PEFT or distilling models to reduce cost and latency

Benefits

Comp & perks

Equal Opportunity Employer committed to fostering a diverse and inclusive workplace
Application process accommodations available upon request