Best suited to experienced ML engineers with NLP and LLM expertise working in risk intelligence and threat detection domains.
Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from Tech Holding via Greenhouse
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 in Mexico to join our team building 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. You’ll collaborate daily with US-based engineers and product stakeholders as an individual contributor.
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
Join daily standups and technical discussions with the US-based client team
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
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)
Multilingual NLP experience, especially Spanish-language text
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)
Previous experience working with US-based clients or distributed teams
Location:
Remote, Mexico
Time Zone:
Must work overlapping hours with US Eastern Time
Employment type:
Contract
#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