• Lead the design, development, and enhancement of forecasting models to improve prediction accuracy and business outcomes. • Build and deploy advanced **Deep Learning** models for large-scale structured and unstructured datasets. • Develop **Generative AI / Explainable AI** solutions to provide transparent and interpretable insights from predictive models. • Analyze healthcare datasets including claims, patient, provider, operational, or clinical data. • Collaborate with business stakeholders, product teams, and engineering teams to translate business challenges into scalable AI solutions. • Monitor model performance, retrain models, and optimize algorithms for production environments. • Mentor junior data scientists and provide technical leadership across the project. • Ensure compliance with healthcare data privacy and governance standards.

ALTEN Mexico is seeking a highly skilled and experienced Data Science and Machine Learning Engineer to join our team. ALTEN Group is a leading engineering and technology company with a strong global presence and over 30 years of industry experience. Operating in 30 countries with a team of more than 46,000 engineers, we deliver cutting-edge solutions for various industries including Automotive, Energy, Aeronautics, Banking & Insurance, Telecom & Multimedia, and Rail. As a Senior Data Science and Machine Learning Engineer at ALTEN Mexico, you will play a crucial role in developing and implementing advanced data-driven solutions. You will work closely with cross-functional teams, leveraging your expertise in data science and machine learning techniques to solve complex business problems.

We are seeking a highly skilled Data Scientist with deep expertise in modern AI/ML systems, including LLMs, multimodal models, fine-tuning techniques, and advanced retrieval architectures. In this role, you will design, prototype, and deploy AI-powered solutions that leverage state-of-the-art language, vision, and agentic frameworks. You will work closely with engineering, product, and research professionals across the US and Europe to bring cutting-edge AI capabilities into production environments. Your Responsibilities: - Design, build, and optimize LLM-powered systems using OpenAI, Anthropic, and open-source/local model families. - Architect and implement RAG pipelines, including hybrid search, query rewriting, prompt optimization, and reranking strategies. - Develop and maintain vector database infrastructures (Pinecone, Weaviate, Qdrant) for large-scale embedding storage and fast retrieval. - Train, evaluate, and retrain embedding models for domain-specific semantic search and knowledge retrieval. - Build and integrate multimodal AI solutions using OCR, CLIP, and modern vision architectures for text-image understanding. - Apply fine-tuning techniques (LoRA/QLoRA) to adapt foundation models to organizational datasets and specialized tasks. - Develop production-ready AI applications using Python, PyTorch, and modern orchestration frameworks. - Implement LLM orchestration with LangChain or LlamaIndex, including evaluators, tool abstractions, memory, and RAG components. - Establish robust evaluation frameworks to measure model performance, reduce hallucination, and ensure reliability in production. - Build agentic workflows using AutoGen, CrewAI, or similar frameworks to power automation and multi-agent collaboration systems. - Stay current with research trends and apply theoretical and practical insights in Generative AI to drive innovation across the organization. What we look for: - Experience in applied machine learning or data science, with at least 2 years focused specifically on LLMs or Generative AI. - Demonstrated experience building end-to-end RAG, fine-tuning, or multimodal AI systems. - Strong proficiency in Python, PyTorch, and AI tooling ecosystems. - Experience deploying models at scale in production environments. - Strong understanding of evaluation metrics, model reliability, and safety/reduction of hallucination. - Familiarity with vector embeddings, vector databases, and semantic search. - Experience with agent frameworks such as AutoGen, CrewAI, or LangGraph-like toolkits. - Experience with distributed training, model optimization, quantization, or GPU acceleration. - Knowledge of DevOps/MLOps tooling for deploying LLM-based systems. - Contributions to open-source LLM or RAG projects. What we offer: - Competitive salary and performance-based bonuses. - Fully remote, flexible work environment. - Modern laptop and hardware provided by us. - Specialized training in AI, automation, and digital productivity tools. - Global exposure—collaborate with top-tier founders and fast-growing startups. - Continuous learning and career growth opportunities in an international environment.

• Develops scalable software solutions utilizing statistical, artificial intelligence (AI) and machine-learning (ML) modeling approaches in support of Fraud, Waste, & Abuse (FWA) detection • Identifies, accesses, compiles, and uses internal and external sources of relevant data • Performs research and testing to develop machine learning algorithms and predictive models • Conducts and reviews model training and evaluation • Integrates, tests, tunes, and monitors solutions • Design and develop data models to predict member outcomes or future impact of key member decisions • Design and construct analysis tools that extract, and analyze data and store analytical results in an appropriate format for business needs • Conduct exploratory data analysis from complex data sources and build key data sets to support Centene’s mission • Evaluate and design experiments to monitor key metrics and identify improvement opportunities • Develop mathematical and statistical models to distinguish relevant content or events and recognize patterns • Participate in presentations and communicate results of analysis and findings • Participate in the design of automated, operational analytics processes to achieve scale and durability of analysis processes • Validate and measure the outcomes of health management programs using SAS, R and other tools, to include provider data, claims data, membership data • Manage multiple projects as assigned • Assist with training Data Analysts.