Tech Holding - Machine Learning Engineer | Contract | Remote
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 • 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: • Employment type: • 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 • 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
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
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