Job Posting Title Principal Software Engineer/Principal AI Engineer

Elsevier; Philadelphia (Philadelphia County); On-site; USD 180,000 - 240,000; Full time

Job summary

Elsevier in Philadelphia seeks a Principal AI Engineer to lead an embedded Innovation Squad from discovery through production. You will assess and harden AI prototypes, deliver new ones and establish agentic AI patterns, orchestration, memory and retrieval for reuse across squads.

You will mentor engineers, finalize requirements with stakeholders, and codify validated solutions into patterns, reference implementations and starter kits while maintaining security and data governance commitments.

Qualifications

• 10+ years of software engineering with hands-on AI system design, build, or integration.
• Experience taking AI prototypes to production in enterprise environments.
• LLM orchestration, prompt engineering, context-window management and production guardrails.
• Integration of AI into enterprise systems via APIs and data pipelines with strong security and data governance."
• Cloud platforms (AWS, Azure, GCP) including managed AI/ML services.
• Delivery experience with Agile, CI/CD, modern SDLC, and TDD.
• Modeling across relational, columnar and vector stores with solid architectural grounding.
• Ability to navigate ambiguity, frame problems, extract specs, and ship validated outcomes quickly.
• Leadership and delivery management to coordinate work, dependencies and blockers.
• Strong communication and influence to drive technical decisions across teams.
• Budget awareness and collaboration with external resources and managed services.
• Establishing baselines and instrumentation to track productivity, cost and quality across pilots to scale.

Responsibilities

• Owning technical execution from discovery to production; build, assess, and harden prototypes and proofs of concept, and deliver new ones.
• Designing and governing reusable agentic AI patterns (orchestration, tool use, memory, retrieval).
• Ensuring delivery meets architecture, security and data-governance standards; recommend infrastructure, hosting and data residency.
• Codifying validated solutions into patterns, reference implementations, and starter kits for reuse across functions.
• Providing design input and finalizing requirements; work with internal stakeholders and end users in the host function.
• Writing specifications, fixing complex bugs, and designing complex data models.
• Acting as the primary technical contact for external resources and the squad's go-to on coding and AI.
• Establishing baselines and reporting measurable outcomes for every initiative.
• Maintaining visibility of in-flight AI pilots to prevent duplication and surface candidates for reuse.
• Ensure alignment with enterprise security and central technology infrastructure standards.
• Continuously evaluate emerging technologies and mentor team members.

Skills

Software engineering

LLM orchestration

Enterprise integration

Cloud platforms

Delivery & SDLC

Data architecture

Leadership

Communication

Commercial awareness

Measurement

Job description

About the team:

Embedded Innovation Teams are cross-functional squads embedded within our segments to rapidly turn internal AI experimentation into validated, reusable solutions, building the capabilities we need to deliver customer value and growth. We work problem-first rather than tool-first, directly inside segment and function teams, improving the internal workflows that help our people deliver better outcomes for customers, faster.

About the role:

As a Principal AI Engineer, you will be the technical lead for an embedded Innovation Squad, owning technical execution from discovery through to production. You will assess and harden existing AI prototypes, deliver new ones, and establish the agentic AI patterns, orchestration, tool use, memory, and retrieval that other squads reuse. You are the senior source of expertise on AI architecture, integration, and delivery.

You will work directly with internal stakeholders and end users in the host function, finalizing requirements, making the architectural calls, and codifying validated solutions into reusable patterns, reference implementations and starter kits. You may also provide sizing, infrastructure, and budget recommendations, and you will mentor and elevate the engineers around you.

Key Responsibilities:

• Owning technical execution from discovery to production; build, assess, and harden prototypes and proofs of concept, and deliver new ones.
• Designing and governing reusable agentic AI patterns (orchestration, tool use, memory, retrieval).
• Ensuring delivery meets architecture, security and data-governance standards; recommend infrastructure, hosting and data residency.
• Codifying validated solutions into patterns, reference implementations, and starter kits so they can be picked up and scaled across other functions.
• Providing design input, finalizing requirements, and work directly with internal stakeholders and end users in the host function.
• Writing specifications, fix complex bugs, and design complex data models.
• Acting as the primary technical contact for external resources and the squad's go-to on coding and AI.
• Establishing baselines and report measurable outcomes for every initiative.
• Maintaining visibility of in-flight AI pilots within the function to prevent duplication and surface candidates for reuse.
• Ensure alignment with enterprise non-negotiables: security, compliance and central technology infrastructure standards.
• Continuously evaluate emerging technologies and mentor and elevate team members.

Requirements:

• Engineering experience: 10+ years in software engineering, with hands-on AI system design, build, or integration, and a track record of taking AI prototypes to production in enterprise environments.
• Agentic AI / LLMs: LLM orchestration, prompt engineering, context-window management and production guardrails; MCP, tool-augmented agents and RAG at enterprise scale.
• Enterprise integration: Integrating AI into enterprise systems via APIs and data pipelines, with strong security, data governance and residency practices.
• Cloud: AWS, Azure, or GCP, including managed AI/ML services.
• Delivery: Agile, CI/CD, modern SDLC, and TDD.
• Data and architecture: Modelling across relational, columnar and vector stores, grounded in sound architectural principles.
• Ways of working: Thrives in ambiguity, framing the problem, extracting defined spec, and prioritises shipping validated outcomes over perfect solutions, moving pilots to production at pace.
• Leadership and delivery management: Plans and sequences work, manages scope, dependencies and priorities, removes blockers, and drives delivery to keep innovation moving.
• Communication and influence: Presents concisely to senior management, host-function stakeholders, and cross-functional teams, and builds buy-in and drives technical decisions across.
• Commercial awareness: Budget-aware; partners with internal and external resources, including managed services and staff augmentation.
• Measurement: Establishes baselines, and instruments solutions to track productivity, cost, and quality outcomes from pilot through scale.