Senior AI Fullstack Engineer

Scope of Services

AI Solution Delivery

• Design, 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 Engineering

• Work 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 Expertise

• Apply 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.

Qualifications

• Bachelor’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 Qualifications

• Experience 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.