Guardian Life Insurance Company: Principal Forward Deployed Engineer
Location: New York/New Jersey Area (Hybrid)

Position Overview:
We are seeking a Principal AI Forward Deployed Engineer (FDE) – an individual contributor at the Principal level – to bridge advanced AI capabilities with Guardian’s business needs and deliver cutting-edge AI solutions across the enterprise. This forward-deployed, builder-led role combines deep architectural expertise with hands-on engineering and close partnership with business stakeholders to drive outcomes. You will be a senior technical authority in our AI Platform Engineering team (reporting to the Head of AI Platform Engineering & Automation) and work directly with leaders in business units like Distribution, Underwriting, Claims, Customer Service, and Operations. Your mission is to design, build, and deploy production-grade AI and agentic systems that embed into real business workflows to deliver measurable business value. You will operate under ambiguity, make high-judgment technical decisions balancing speed and long-term strategy, and ensure Guardian’s AI solutions are pragmatic, scalable, and aligned with enterprise standards. This is a rare opportunity to take a builder’s mindset and bias for action into a Fortune 250 environment – applying the technical leadership, the hands-on architecture and development skill of a Principal Engineer, and the customer-focused, end-to-end ownership of a Forward Deployed Engineer.

Key Responsibilities:

• AI Solution Architecture & Delivery: Design and own the architecture for end-to-end AI systems – from data ingestion and model development to service integration and production deployment. Build and deploy AI/ML-driven applications (including LLM-powered agents, automation workflows, and predictive analytics) that integrate seamlessly into business processes and legacy environments to drive operational outcomes.
• Forward-Deployed Project Leadership: Serve as the technical lead on forward-deployed initiatives across various business units. Partner directly with business stakeholders (from department leads to senior executives) to understand business goals, translate them into technical solutions, and deliver those solutions in fast iteration cycles. Adapt quickly to evolving requirements and ensure solutions solve the right problem with tangible business impact.
• Hands-On Development & Prototyping: Act as a hands-on builder – write production-quality code, quickly prototype new ideas, and lead by example in solving complex technical problems. Transform prototypes into secure, compliant, and scalable production systems in cloud environments (AWS), employing best practices in software engineering, DevOps, and MLOps.
• Strategic Technical Decisions & Vendor Integration: Evaluate build vs. buy decisions with a high degree of judgment. Stay current on emerging AI and data technologies; rapidly assess third-party vendors and open-source solutions for technical fit and strategic value. When appropriate, integrate vendor products to accelerate delivery – architecting for optionality (design solutions to easily plug-and-play or swap vendors) while maintaining alignment with Guardian’s AI Platform strategy and enterprise architecture standards.
• Enterprise Architecture & Standards: Ensure all AI solutions meet Guardian’s high standards for scalability, reliability, security, and compliance. Work closely with data and security teams to architect solutions that protect customer data and comply with regulatory requirements (e.g., privacy, model governance) without stifling innovation. Champion best practices in AI engineering, code quality, and system design to raise the bar across teams.
• Technical Leadership & Influence: Though an individual contributor, act as a senior technical leader within the organization. Conduct architecture reviews and design guidance for multiple teams, mentoring engineers and sharing expertise in AI engineering. Influence without formal authority by driving consensus on technical approaches and long-term AI strategy across cross-functional teams. Communicate complex technical concepts and roadmaps clearly to both technical audiences and senior non-technical stakeholders, including executive leadership.
• End-to-End Ownership & Operational Excellence: Take ownership of outcomes – not just delivering code, but ensuring the solutions achieve the intended business results in production. Oversee the full lifecycle of AI solutions, from initial requirements and PoC to ongoing operation, monitoring, and continual improvement in production. Troubleshoot and resolve issues in real time in live environments, building resilient systems and establishing metrics and feedback loops to drive continuous improvement.

What We’re Looking For (Qualifications):

• Proven Experience in AI & Software Engineering: Extensive experience (typically 8+ years) in software development and architecture, including building AI/ML solutions or large-scale systems in production. Demonstrated success designing and delivering complex systems end-to-end in a high-scale or enterprise environment.
• Enterprise Architectural Depth: Expertise in system design and architecture – able to craft scalable, maintainable designs for distributed systems, data pipelines, and cloud services. Comfortable making high-impact architectural decisions (understanding which are one-way door vs. two-way door decisions) that balance immediate needs with long-term maintainability.
• Hands-On Technical Leadership: Track record of technical leadership in engineering projects – leading design, guiding implementation, and setting technical direction across teams. Skilled at solving ambiguous problems with innovative, well-structured solutions. Passionate about coding and code quality with proficiency in languages and frameworks commonly used for AI (e.g., Python).
• AI/ML & Data Expertise: Strong working knowledge of AI/ML techniques (e.g., machine learning pipelines, model training & evaluation, large language models, generative AI) and experience bringing models or algorithms into production applications. Familiarity with MLOps practices for model deployment and lifecycle management is a plus.
• Cloud & DevOps: Deep experience with cloud platforms (especially AWS) for data and AI solutions, including leveraging cloud-native services for computing, data storage, and AI (for example, AWS SageMaker, Lambda, EMR, etc.). Proficient in modern DevOps/engineering practices – CI/CD, containerization, infrastructure-as-code – ensuring solutions are robust and maintainable in production.
• Business Acumen & Stakeholder Collaboration: Exceptional ability to engage with non-technical stakeholders and senior leadership. Can translate between business objectives and technical strategy seamlessly. Strong communication skills to convey technical ideas in business terms and inspire confidence and alignment. Prior experience working in or with regulated industries (insurance, finance, healthcare) is valuable – you understand how to innovate within enterprise guardrails and handle sensitive data responsibly.
• Bias for Action & Ownership Mindset: You exhibit a bias for action and a builder’s mindset – proactively identifying opportunities, rapidly prototyping solutions, and driving them to completion. You take ownership of outcomes, persisting through obstacles, and are motivated by seeing your technology make a measurable impact on the business.

Technology & Environment (Example Tools & Platforms):
This role operates within Guardian’s modern cloud and data ecosystem. While not all tools are required, experience with similar technologies will be beneficial:

• Cloud & Infrastructure: AWS (Amazon Web Services) – e.g., EC2, S3, Lambda, SageMaker, Bedrock, EKS; Infrastructure-as-Code tools (CloudFormation/Terraform).
• Data & Integration: Modern data platforms and streaming – e.g., Snowflake, Redshift, Kafka/Kinesis, Spark/Databricks, relational and NoSQL databases; Data processing and pipeline frameworks (AWS Glue, Apache Airflow, etc.).
• AI & ML Tooling: Large Language Models and AI services (OpenAI GPT, Anthropic, AWS AI services), machine learning libraries/frameworks (PyTorch, TensorFlow, Scikit-learn), and MLOps pipelines.
• DevOps & Monitoring: Containers and orchestration (Docker, Kubernetes), CI/CD pipelines (Jenkins/GitHub Actions), observability tools (CloudWatch, Datadog, etc.) to ensure high operational excellence.

Salary Range:

The salary range reflected above is a good faith estimate of base pay for the primary location of the position. The salary for this position ultimately will be determined based on the education, experience, knowledge, and abilities of the successful candidate. In addition to salary, this role may also be eligible for annual, sales, or other incentive compensation.