Machine Learning Engineer | Contract | Remote

Tech Holding; Dallas (TX); Remote; USD 165,000 - 248,000; Part time

Job summary

Tech Holding is seeking a hands-on Machine Learning Engineer for a remote, project-based assignment to build an AI-powered risk intelligence platform. You will design, train, and deploy NLP and LLM-based models processing large multilingual data, collaborating with data and backend teams.

You will deploy scalable ML services with strict latency and cost targets, evaluate model bias and drift, and communicate trade-offs clearly to stakeholders. Must work overlapping US Eastern hours.

Qualifications

• 4+ years of professional experience building ML models in production.
• Strong Python skills and hands-on experience with PyTorch, TensorFlow, or Hugging Face Transformers.
• Practical NLP experience (classification, NER, topic modeling, semantic similarity).
• Hands-on experience with LLMs (OpenAI, Anthropic, or open-source models) including prompt engineering and RAG.
• Experience with embeddings and vector search (pgvector, Pinecone, OpenSearch, FAISS).
• Experience deploying ML services on AWS, GCP, or Azure with Docker and CI/CD.
• Solid understanding of model evaluation, metrics, test sets, and production monitoring.

Responsibilities

• Design, train, and deploy ML models for text classification, entity extraction, clustering, and topic detection.
• Build and improve LLM-powered features, including prompt design and retrieval-augmented generation.
• Develop embedding and semantic-search pipelines to surface emerging threats.
• Productionize models as scalable, monitored services with latency, cost, and accuracy targets.
• Create evaluation frameworks to measure model quality, bias, and drift.
• Collaborate with data engineers to improve ingestion and feature pipelines for high-volume text data.
• Monitor and retrain models in production and debug quality issues end to end.
• Document approaches and trade-offs for technical and non-technical stakeholders.

Skills

Python

PyTorch

TensorFlow

Hugging Face

NLP

LLMs

Embeddings

Vector search

Docker

CI/CD

AWS

GCP

Azure

MLOps

LangChain

LlamaIndex

Kafka

Spark

Tools

pgvector

Pinecone

OpenSearch

FAISS

Job description

USA, Remote

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

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