{"id":2411461,"url":"https://alion.io/job/tech-holding-machine-learning-engineer-contract-remote-mx","title":"Machine Learning Engineer | Contract | Remote, MX","company":{"id":6381,"name":"Tech Holding","domain":"techholding.co","url":"https://alion.io/company/tech-holding","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"contractor","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mexico"],"countries":["MX"],"hiring_countries":["MX"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FAISS","optional":false},{"name":"GCP","optional":false},{"name":"Hugging Face","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NER","optional":false},{"name":"NLP","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenSearch","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"TensorFlow","optional":false},{"name":"Transformers","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"Apache Kafka","optional":true},{"name":"LangChain","optional":true},{"name":"LlamaIndex","optional":true},{"name":"MLFlow","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Spark","optional":true},{"name":"Vertex AI","optional":true}],"status":"live","first_seen_at":"2026-10-12T23:57:14Z","employer_posted_date":"2026-10-12","last_verified_at":"2026-10-13T15:09:17Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About us:\nWorking 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.\nThe Role:\nWe’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.\nKey Responsibilities:\nDesign, train, and deploy ML models for text classification, entity extraction, clustering, and narrative and topic detection\nBuild and improve LLM-powered features, including prompt design, retrieval-augmented generation (RAG), and agentic workflows\nDevelop embedding and semantic-search pipelines to group related content and surface emerging threats\nProductionize models as scalable, monitored services with clear latency, cost, and accuracy targets\nBuild evaluation frameworks and datasets to measure model quality, bias, and drift over time\nWork with data engineering to improve ingestion, labeling, and feature pipelines for high-volume, noisy, multilingual text data\nMonitor and retrain models in production, and debug quality issues end to end\nJoin daily standups and technical discussions with the US-based client team\nRequirements:\n4+ years of professional experience building and deploying machine learning models in production\nStrong Python skills and hands-on experience with PyTorch, TensorFlow, or Hugging Face Transformers\nPractical NLP experience, such as classification, named entity recognition (NER), topic modeling, or semantic similarity on real-world text data\nHands-on experience building with LLMs (OpenAI, Anthropic, or open-source models), including prompt engineering and RAG\nExperience with embeddings and vector search (e.g., pgvector, Pinecone, OpenSearch, or FAISS)\nExperience deploying and operating ML services on AWS, GCP, or Azure, using Docker and CI/CD\nSolid grasp of model evaluation, including metrics, test sets, error analysis, and production monitoring\nNice to have:\nExperience with trust and safety, threat intelligence, disinformation research, or social media data\nExperience building agentic AI systems or tool-using LLM workflows (e.g., LangChain, LlamaIndex, or custom frameworks)\nMultilingual NLP experience, especially Spanish-language text\nFamiliarity with MLOps tooling such as MLflow, SageMaker, Vertex AI, or Weights & Biases\nExperience with streaming or large-scale data processing (e.g., Kafka, Spark, or Airflow)\nPrevious experience working with US-based clients or distributed teams\nLocation: \nRemote, Mexico\nEmployment type:\nContract\nTech 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. 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