Staff AI Engineer - Data & Portfolio Planning

We are seeking a highly experienced Staff AI Engineer to lead the architecture, development, and delivery of next-generation AI, data, and analytics solutions for product portfolio planning and investment decision-making.

This is a hands-on technical leadership role for an engineer who can operate across the full data-to-AI stack, from enterprise data architecture and large-scale data engineering to GenAI/LLM applications, AI agents, advanced analytics, and executive-facing decision-support products.

The ideal candidate combines strong software engineering and AI capabilities with deep experience in enterprise data, portfolio/product planning, and business analytics. You will work closely with product leaders, business stakeholders, data scientists, architects, and engineering teams to transform complex enterprise data into intelligent products that help organizations make better portfolio, product, manufacturing, and investment decisions.

Key Responsibilities

Data Engineering & Architecture

• Architect and implement scalable data platforms supporting portfolio planning, product analytics, forecasting, and investment decisions.
• Design conceptual, logical, and physical data models for complex enterprise data domains.
• Build high-volume data pipelines using technologies such as Azure, Databricks, Spark, Delta Lake, and SQL
• Integrate data from multiple enterprise systems and create trusted, reusable data products and analytical datasets.
• Establish data lineage, data dictionaries, governance, quality standards, and reusable enterprise data assets.
• Optimize data pipelines and analytical workloads for performance, scalability, reliability, and cost.

AI Engineering & Agentic Applications

• Architect, develop, and productionize GenAI/LLM applications and AI agents for enterprise use cases.
• Design multi-agent workflows that can reason over enterprise data, documents, analytics, and business knowledge.
• Build AI-powered solutions that automate analysis, generate insights, synthesize information, and support complex portfolio-planning decisions.
• Evaluate and integrate emerging AI technologies, LLM platforms, agent frameworks, retrieval-augmented generation, and enterprise AI tools.
• Develop reliable, secure, observable, and scalable AI applications suitable for enterprise production environments.
• Establish engineering patterns and best practices for AI application development, evaluation, governance, and deployment.

Portfolio Planning & Product Analytics

• Partner with portfolio and product leadership to translate strategic questions into data- and AI-driven solutions.
• Develop analytics products that support:

• Product portfolio planning
• Investment prioritization
• Product roadmap analysis
• Long-term forecasting
• Scenario planning
• Market and customer insights
• Manufacturing and supply planning
• Transform complex analytical outputs into intuitive dashboards, decision-support applications, and AI-assisted experiences.
• Develop scenario modeling and forecasting capabilities that enable leaders to evaluate long-term business and product strategies.
• Connect portfolio strategy with data, analytics, financial considerations, product development, and operational constraints.

Software Engineering

• Personally contribute production-quality code in Python, SQL, Spark, and related technologies.
• Build reusable frameworks, services, pipelines, APIs, and AI components.
• Establish engineering standards for code quality, testing, CI/CD, observability, security, and maintainability.
• Perform code reviews, performance tuning, technical troubleshooting, and production support.
• Drive solutions from concept and architecture through development, deployment, adoption, and continuous improvement.

Technical Leadership

• Serve as a senior technical authority across AI, data, analytics, and portfolio-planning initiatives.
• Translate business and product strategy into scalable technical architectures and executable engineering roadmaps.
• influence architecture and technology decisions across multiple teams and organizational boundaries.
• Mentor senior engineers, architects, and data/AI practitioners.
• Define technical standards, reference architectures, reusable patterns, and best practices.
• Communicate complex technical concepts clearly to both engineering teams and executive stakeholders.
• Lead technical discussions and create alignment among product, business, data, AI, security, and engineering organizations.