Job description

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

Requirements

Must-haves

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
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
Must work overlapping hours with US Eastern 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

Nice-to-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaillmmlpythonawsgcpazuredockerci/cd

Platform: greenhouse
Effort: quick