Applied AI Solutions Technical Lead

What You Will Do

Guide IT application teams in identifying and designing applied AI, machine learning, and generative AI capabilities that address practical business needs.

Translate business requirements into reusable AI solution patterns, reference designs, and implementation guidance for delivery teams.

Create focused proofs of concept and prototypes to demonstrate emerging AI capabilities and validate feasibility.

Ensure proposed AI capabilities are practical, maintainable, reliable, scalable, and aligned to enterprise architecture standards.

Provide hands-on coaching and technical consultation so teams can adopt AI responsibly and consistently.

Design and implement RAG pipelines, including ingestion, embeddings, retrieval strategies, and grounding.

Integrate enterprise knowledge sources while respecting data classification, privacy requirements, access controls, and security boundaries.

Partner with IT application, architecture, data, cybersecurity, infrastructure, and platform teams to move approved AI capabilities into production environments.

Support application teams through SDLC, CI/CD, API integration, testing, monitoring, versioning, documentation, and operational readiness for AI-enabled solutions.

Ensure solutions meet enterprise standards for performance, resilience, security, privacy, supportability, and cost management.

Establish operational guardrails, implementation standards, and support practices for consistent enterprise adoption.

Please note, salary range is not inclusive of total compensation. The expected salary range for this position will be impacted by factors such as the successful candidate’s skills, experience and working location, as well as the specific position’s business line, scope and level. Candidates with salary expectations outside of the range are encouraged to apply.

Who You Are

Bachelor’s degree in Information Security, Computer Science, or a related discipline.

5+ years of experience in machine learning, AI, data science, software engineering, or applied AI solution delivery, with demonstrated experience moving solutions from concept to production.

Experience in insurance, financial services, or other regulated environments.

Familiarity with cloud AI platforms, vector databases, AI agent frameworks, and model integration approaches such as MCP or similar orchestration patterns.

Experience measuring business outcomes from AI solutions, such as productivity improvement, reduced manual effort, improved knowledge reuse, or enhanced decision support.

Hands-on experience with: Generative AI and large language models Prompt engineering techniques RAG architectures and vector databases Model and system evaluation frameworks, including accuracy, relevance, safety, latency, cost, and user feedback measures Secure integration patterns for enterprise systems, APIs, data sources, and AI orchestration frameworks

Generative AI and large language models

Prompt engineering techniques

RAG architectures and vector databases

Model and system evaluation frameworks, including accuracy, relevance, safety, latency, cost, and user feedback measures

Secure integration patterns for enterprise systems, APIs, data sources, and AI orchestration frameworks

Strong programming skills in Python and modern AI/ML frameworks.

Experience deploying AI solutions into production environments using disciplined SDLC, DevOps, MLOps, or platform engineering practices.

Please note that internal applicants are asked to formally express their interest by applying online no later than 10 business days of the position being posted.

Please note that we may use AI tools to help us through the recruitment process.