AI Engineering Lead

As the AI Engineering Lead, you will architect, build, and scale AI-powered solutions that accelerate both the delivery of Integrated Services Data, AI & Analytics (ISDAIA) products and the adoption of AI capabilities across the Integrated Services business.

You will lead the development of enterprise-grade AI applications, agentic systems, copilots, retrieval-augmented generation (RAG) solutions, and intelligent workflow automation that transform how teams discover insights, make decisions, and deliver value.

This role combines hands-on technical leadership with product thinking and strategic execution. You will partner closely with Product Managers, Engineering Teams, Analytics Leaders, and Business Stakeholders to identify high-value use cases, develop reusable AI capabilities, and enable responsible AI adoption at scale.

You will play a key role in shaping the future AI ecosystem for Integrated Services by building scalable frameworks, shared services, and AI-enabled experiences that improve business outcomes, operational efficiency, and speed to delivery.

Minimum Requirements

• Bachelor’s Degree in a related field (Computer Science, Artificial Intelligence, Data Science, Engineering, Information Technology, or equivalent).

• 5 to 8 years of experience delivering enterprise software, analytics, data, or AI solutions.

• 5+ years of experience using Python-based development technologies and modern software engineering practices.

• 3+ years of experience designing, deploying, and supporting AI/ML or Generative AI solutions in production environments.

• Experience building and deploying Generative AI, conversational AI, Copilot, or agent-based solutions.

• Experience acting as a senior technical lead facilitating solution trade-offs and architectural decisions.

• Experience using Cloud AI Platforms (GCP preferred).

• Strong understanding of APIs, cloud-native architectures, CI/CD pipelines, and enterprise application development.

• Hands-on experience with Generative AI technologies, Retrieval-Augmented Generation (RAG), and enterprise AI deployment.

Preferred Requirements

• Master’s Degree in Artificial Intelligence, Computer Science, Data Science, Engineering, or related field.

• Experience managing and growing high-performing AI engineering teams.

• Experience developing enterprise copilots, AI assistants, agent-based systems, and intelligent automation solutions.

• Experience implementing Retrieval-Augmented Generation (RAG) architectures, vector databases, semantic search, and knowledge-grounding strategies.

• Strong working knowledge of GCP and enterprise AI architecture patterns.

• Expertise in open-source technologies such as Python, LangChain, LangGraph, Semantic Kernel, SQL, Spark, and modern AI development frameworks.

• Experience working with Vertex AI, OpenAI, Anthropic, Gemini, or comparable enterprise AI ecosystems.

• Experience building reusable AI platforms, accelerators, frameworks, and enablement capabilities.

• Experience deploying AI solutions into business workflows, analytics products, self-service insights platforms, or decision-support solutions.

• Experience implementing Responsible AI, AI governance, MLOps, and LLMOps practices at enterprise scale.

• Immediate medical, dental, vision and prescription drug coverage

• Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care and more

• Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more

• Vehicle discount program for employees and family members and management leases

• Tuition assistance

• Established and active employee resource groups

• Paid time off for individual and team community service

• A generous schedule of paid holidays, including the week between Christmas and New Year's Day

• Paid time off and the option to purchase additional vacation time

Strategic Thinking & Leadership

• Partner with business leaders and product teams to identify high-value AI opportunities and translate them into scalable AI-powered solutions.

• Define and communicate AI solution vision, roadmaps, and measurable success metrics.

• Drive AI strategy across Generative AI, Agentic AI, conversational experiences, AI-enabled analytics, and intelligent automation initiatives.

• Establish governance frameworks for Responsible AI, security, compliance, scalability, and enterprise adoption.

• Lead cross-functional AI programs and influence executive stakeholders through compelling business cases, demonstrations, and measurable outcomes.

Technical Leadership & Expertise

• Architect and oversee end-to-end AI solutions, including:

  • Conversational AI and Copilot experiences

  • Retrieval-Augmented Generation (RAG) architectures

  • Agentic AI frameworks and multi-agent orchestration systems

  • AI-powered analytics and insight generation solutions

  • Natural language interfaces for analytics and business intelligence

  • Intelligent workflow automation and decision-support capabilities

  • Semantic search and enterprise knowledge management solutions

• Strong proficiency in Google Cloud Platform (GCP) services for AI development (Vertex AI, BigQuery, Cloud Storage, Dataflow).

• Experience designing and deploying enterprise AI solutions leveraging Large Language Models (LLMs), foundation models, prompt engineering, and model evaluation frameworks.

• Experience building AI systems using Python-based ecosystems and modern AI frameworks.

• Experience with vector databases, embeddings, semantic search, grounding techniques, and retrieval architectures.

• Implement scalable AI Engineering, MLOps, and LLMOps practices including CI/CD, prompt versioning, testing, governance, monitoring, and lifecycle management.

• Proficiency in Git, Docker, API-based deployments, cloud-native architectures, and scalable AI services.

• Apply strong software engineering practices including modular design, testing, observability, security, and documentation.

• Establish reusable AI frameworks, accelerators, and engineering patterns that improve speed, consistency, and quality of delivery.

• Evaluate emerging AI technologies and identify opportunities to accelerate analytics delivery and business adoption.

• Support architectural reviews and ensure best practices across AI systems, platforms, and products.

• Implement Responsible AI principles including governance, explainability, privacy, security, and ethical AI compliance.

Delivery Focus

• Own end-to-end AI solution delivery in partnership with Product, Engineering, Data, and Business teams.

• Ensure production-grade deployment of AI applications, copilots, and agent-based solutions using containerization, orchestration, and scalable cloud infrastructure.

• Build reusable AI accelerators, frameworks, and services that improve speed-to-delivery across the ISDAIA portfolio.

• Partner with product teams to embed AI capabilities directly into dashboards, self-service analytics platforms, applications, and business workflows.

• Influence investment decisions using measurable business impact, adoption metrics, operational efficiencies, and ROI analysis.

• Establish monitoring frameworks for AI performance, solution effectiveness, reliability, governance, and user adoption.

Team Development & Community Leadership

• Lead and mentor AI engineers while establishing best practices for enterprise AI development.

• Build AI engineering standards, reusable frameworks, shared tooling, libraries, and delivery patterns across ISDAIA.

• Promote knowledge sharing through Communities of Practice and AI Centers of Excellence.

• Foster a culture of experimentation, continuous learning, innovation, and engineering excellence.

• Support talent development in emerging AI disciplines including Generative AI, Agentic AI, conversational experiences, and intelligent automation.

• Serve as a thought leader for enterprise AI adoption and AI-enabled transformation initiatives.