AI Platform Engineer; Google Cloud Platform

Senior Staff Software Engineer - IE07HE

We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.

The Hartford seeks energetic and passionate AI Platform Engineers to build AI Operations (AIOps, MLOps , FMOps , LLMOps ) services for our AI Platform team. Our team supports data science and business teams across the enterprise, driving streamlined processes and smarter decision-making in functions like Actuarial, Product, Underwriting, and Sales. As an AI Engineer within the AI Operations organization, you will play a significant role in delivering modern and sustainable data science products generating meaningful outcomes for the enterprise.

We are looking for talent who embraces our core values:

Solution-Oriented: We build solutions, not just models, focusing on end-to-end business problems and systems design.
Trust and Transparency: We collaborate closely with partners, mindful of their capacity to absorb change.
Reliability: Our products come with full monitoring solutions to ensure they deliver as expected.
Influence through Humility: We listen carefully to our customers and become partners in problem-solving.
Practical and Evolutionary: We deliver minimally viable products first and expand their sophistication over time based on feedback.

This role can have a Hybrid or Remote work arrangement. Candidates who live near one of our office locations will have the expectation of working in an office 3 days a week (Tuesday through Thursday) . Candidates who do not live near an office will have a remote work arrangement, with the expectation of coming into an office as business needs arise.

Responsibilities:

Develop value-added features tailored to company specific work, above and beyond the core capabilities of the cloud platform and relevant vendor tools.

Research, experiment with, and implement suitable GenAI algorithms, tools, and technologies.

Explore new services and capabilities in AWS, Google Cloud Platform, and Azure to support GenAI and ML services.

Enhance platform functionality with strong engineering expertise in AI, ML, Agentic Frameworks, and modern data technologies.

Develop and promote best practices in AI, ML, and data engineering across teams.

Architect and design end-to-end solutions at a component level.

Collaborate with partners in Enterprise Data, Data Science, Business, Cloud Enablement Team, and Enterprise Architecture teams.

Manage engineering tasks, driving execution, and optimizing workflows with minimal guidance.

Provide technical mentorship and career growth opportunities for team members.

Review work of systems-level engineers to calibrate deliverables against project and business expectations.

Qualifications:

• Bachelor's degree in Computer Science, Computer Engineering, or a technical field.
• 8+ years building and shipping software and/or platform solutions for enterprises.

• Programming experience with Python is preferred .

• 3 + years of experience with Terraform.
• Experience building libraries, frameworks or platforms used across multiple teams is a plus.

• Proven experience with Google Cloud Platform (GCP).

• Experience with GCP Big Query , Cloud Functions, AI Platform, API Gateway , GKE /Docker is a must .

• Proven experience in working with other cloud providers such as AWS cloud is a plus.

• Experience with CI/CD pipelines, Automated Testing, Automated Deployments, Agile methodologies, Unit Testing, and Integration Testing tools.

• Experience with building scalable serverless applications (real-time/batch) using cloud technologies . GCP is a plus.

• Knowledge of distributed No
  
  SQL database systems and data engineering, ETL technology.

• Conversational UX/UI design (chatbots) and Human-Agent-Interaction (HAI) is a plus.

• Experience with IR, vector embedding, and Hybrid/Semantic search technologies.
• Knowledge about customization techniques across various stages of the RAG pipeline, including model fine-tuning, retrieval re-ranking, HNSW, and product quantization is a plus.
• Experience with…