Senior AI Fullstack Engineer

Senior AI Fullstack EngineerCapco is seeking experienced and motivated AI Engineers to help design, build, and deploy AI-enabled solutions for clients in the financial services industry.As part of this engagement, you will work on complex business, technology, and data challenges, applying modern AI engineering practices to create scalable, reliable, and production-ready solutions. We are seeking professionals combines strong software engineering skills with practical experience in large language models, machine learning, data platforms, and modern AI application development. You will collaborate with client stakeholders, product teams, data specialists, and engineers to turn business needs into AI capabilities that are explainable, maintainable, and aligned with enterprise standards. Scope of Services AI Solution DeliveryDesign, develop, test, and deploy AI-enabled applications and services.Build solutions using large language models, machine learning, retrieval-augmented generation, semantic search, and agentic AI patterns where appropriate.Translate client business problems into technical designs, implementation plans, and working software.Develop reusable AI components, APIs, prompts, workflows, and evaluation approaches.Ensure AI solutions are scalable, reliable, secure, and suitable for production use.Data, Knowledge, and Retrieval EngineeringWork with structured and unstructured data sources to support AI-enabled discovery, reasoning, and decision support.Build or integrate search, retrieval, ranking, and knowledge management capabilities.Develop approaches that help AI systems understand business context, data assets, metadata, documentation, and other enterprise knowledge sources.Improve the quality, relevance, traceability, and explainability of AI-generated outputs.Collaborate with data engineering and platform teams to ensure AI solutions are well integrated with existing data ecosystems.Technical ExpertiseApply strong programming skills, particularly in Python, to build robust AI and data-driven applications.Use modern AI frameworks, cloud services, APIs, and development tools to deliver production-grade solutions.Implement testing, monitoring, logging, evaluation, and performance optimization for AI workflows.Support integration with cloud-native data and AI platforms, preferably Google Cloud Platform.Apply established software engineering practices, including version control, CI/CD, documentation, and code review.QualificationsBachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Mathematics, Statistics, or a related field.3+ years of experience in AI engineering, software engineering, or machine learning engineering.Strong programming skills in Python.Practical experience building AI applications, ideally including large language models or generative AI.Experience with modern AI engineering patterns such as retrieval-augmented generation, embeddings, semantic search, prompt engineering, model evaluation, or agent-based workflows.Strong understanding of software engineering principles, APIs, data structures, testing, and production deployment.Experience working with cloud platforms, preferably Google Cloud Platform; experience with AWS or Azure is also valuable.Experience with MLOps, LLMOps, CI/CD, automated testing, monitoring, and model evaluation frameworks.Ability to work with structured and unstructured data, including metadata, documentation, logs, or enterprise knowledge sources.Strong analytical, problem-solving, communication, and stakeholder management skills.Ability to work independently and collaboratively in fast-paced client delivery environments.Preferred QualificationsExperience working with financial services clients or in another regulated enterprise environment.Familiarity with data platforms, data catalogs, metadata management, knowledge graphs, vector databases, or search technologies.Experience building AI solutions that require traceability, human review, or strong governance controls.Consulting experience or experience working across multiple stakeholders, teams, and delivery workstreams.