Machine Learning Engineer | Contract | Remote

Tech Holding
United States
Remote

Who this role is best for

Practitioners with NLP and LLM deployment experience in threat intelligence will find this contract role a good fit. The remote setup and collaboration with cross-functional teams suggest a need for self-starters who can work independently while aligning with product and engineering stakeholders.

Best fit for

  • Candidates with NLP and LLM deployment experience in threat intelligence domains
    — “help enterprises detect, assess, and respond to emerging online threats”
  • Individuals who can operate as independent contributors in a remote, cross-functional setup
    — “individual-contributor role working closely with data engineers, backend engineers, and product stakeholders”
  • Professionals with a track record of building scalable, monitored ML services
    — “Productionize models as scalable, monitored services with clear latency, cost, and accuracy targets”

Things to consider

  • US work authorization is mandatory, as visa sponsorship is not available
    — “Applicants must be authorized to work for ANY employer in the U.S.”
  • The role involves handling high-volume, noisy, multilingual text data
    — “improve ingestion, labeling, and feature pipelines for high-volume, noisy, multilingual text data”

How to stand out

  • Highlight hands-on experience with RAG and agentic workflows in your resume
    — “Build and improve LLM-powered features, including prompt design, retrieval-augmented generation (RAG), and agentic workflows”
  • Emphasize your ability to debug end-to-end ML quality issues in production
    — “Monitor and retrain models in production, and debug quality issues end to end”
  • Showcase your work with multilingual NLP and vector search tools like Pinecone or FAISS
    — “Experience with embeddings and vector search (e.g., pgvector, Pinecone, OpenSearch, or FAISS)”
Pace · Fast PacedCollaboration · MediumAutonomy · HighDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • Design, train, and deploy ML models for text classification, entity extraction, clustering, and narrative and topic detection
Typical background
4+ years of professional experience building and deploying machine learning models in production

Skills & requirements

Required

PythonPyTorchTensorFlowHugging Face TransformersNLPLLMPrompt EngineeringRAGEmbeddingsVector SearchML Services DeploymentDockerCI/CDModel EvaluationData IngestionLabelingFeature PipelinesModel MonitoringProductionizationCommunication

Preferred

Trust And SafetyThreat IntelligenceDisinformation ResearchSocial Media DataAgentic AI SystemsMultilingual NLPMlopsStreaming Data ProcessingLLM Fine-tuning

Stack & domain

PythonPyTorchTensorFlowHugging Face TransformersNLPLlmsEmbeddingsVector SearchAWSGCPAzureDockerCI/CDModel EvaluationMlflowSagemakerVertex AIWeights & BiasesStreamingLarge-scale Data ProcessingFine-tuning LlmsCommunicationAIRisk IntelligenceDisinformationReputational AttacksSecurity Risks

About the role

Original posting from Tech Holding via Greenhouse

About us:

Working at Tech Holding isn't just a job, it's an opportunity to be a part of something bigger. We are a full-service consulting firm that was founded on the premise of delivering predictable outcomes and high-quality solutions to our clients.  Our founders and team members have industry experience and have held senior positions in a wide variety of companies – from emerging startups to large Fortune 50 firms – and we have taken our combined experiences and developed a unique approach that is supported by the principles of deep expertise, integrity, transparency, and dependability.

The Role:

We’re looking for a hands-on Machine Learning Engineer, for a project based assignment, to help build an AI-powered risk intelligence platform. The platform helps enterprises detect, assess, and respond to emerging online threats, including disinformation, reputational attacks, and security risks. You’ll design, build, and deploy NLP and LLM-based models that process large volumes of data from mainstream and niche online sources. Your work will feed directly into the signals customers act on. This is an individual-contributor role working closely with data engineers, backend engineers, and product stakeholders.

Key Responsibilities:

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

Requirements:

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 (e.g., 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

Nice to have:

Experience with trust and safety, threat intelligence, disinformation research, or social media data

Experience building agentic AI systems or tool-using LLM workflows (e.g., LangChain, LlamaIndex, or custom frameworks)

Experience with multilingual NLP

Familiarity with MLOps tooling such as MLflow, SageMaker, Vertex AI, or Weights & Biases

Experience with streaming or large-scale data processing (e.g., Kafka, Spark, or Airflow)

Experience fine-tuning LLMs (LoRA or PEFT) or distilling models to cut cost and latency

Location:

Remote, USA

Time Zone: 

Must work overlapping hours with US Eastern Time

Employment type:

Contract

*Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time

#LI-Remote

Tech Holding is proud to be an Equal Opportunity Employer and is committed to fostering a diverse and inclusive workplace. We welcome applicants from all backgrounds and experiences, and we consider qualified applicants without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected characteristic. If you require accommodation in the application process, please contact our HR 

Source: Tech Holding careers (Greenhouse)

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