Machine Learning Engineer

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.

- 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