Machine Learning Engineer | Contract | Remote
Contract, remote (USA) Machine Learning Engineer building an AI-powered risk intelligence platform that detects online threats like disinformation and security risks. Day to day: designing, training, and deploying NLP/LLM models (classification, entity extraction, RAG, embeddings/semantic search) as scalable production services using Python and cloud ML tooling.
- Compensation
- Not disclosed
- Location
- USA, Remote
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Contract
- Experience
- 4+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 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 (classification, NER, topic modeling, or semantic similarity) on real-world text data
- Hands-on experience building with LLMs (OpenAI, Anthropic, or open-source), 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: 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
Full 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