AI Platform Engineer (Google Cloud Platform)
About The Position
The Hartford is seeking energetic and passionate AI Platform Engineers to build AI Operations (AIOps, MLOps, FMOps, LLMOps) services for their AI Platform team. This team supports data science and business teams across the enterprise, aiming to streamline processes and enable smarter decision-making in various functions such as Actuarial, Product, Underwriting, and Sales. As an AI Engineer within the AI Operations organization, the successful candidate will play a significant role in delivering modern and sustainable data science products that generate meaningful outcomes for the enterprise. The company values a solution-oriented approach, focusing on end-to-end business problems and systems design, and emphasizes trust, transparency, and collaboration with partners. Products are expected to be reliable with full monitoring solutions. The team operates with humility, listening to customers and partnering in problem-solving, and follows a practical, evolutionary approach by delivering minimally viable products first and expanding sophistication based on feedback. This role offers a Hybrid or Remote work arrangement. Candidates residing near an office location are expected to work in the office three days a week (Tuesday through Thursday), while those not near an office will have a remote arrangement with occasional office visits as business needs arise.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, or a technical field.
- 8+ years building and shipping software and/or platform solutions for enterprises.
- 3+ years of experience with Terraform.
- Proven experience with Google Cloud Platform (GCP).
- Experience with GCP BigQuery, Cloud Functions, AI Platform, API Gateway, GKE/Docker is a must.
- 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.
- Knowledge of distributed NoSQL database systems and data engineering, ETL technology.
- Foundational understanding of Natural Language Processing and Deep Learning.
- Excellent problem-solving skills and the ability to work in a collaborative team environment.
- Excellent communication skills.
- Candidate must be authorized to work in the US without company sponsorship.
Nice To Haves
- Programming experience with Python is preferred.
- Experience building libraries, frameworks or platforms used across multiple teams is a plus.
- Proven experience in working with other cloud providers such as AWS cloud is a plus.
- GCP is a plus (in the context of building scalable serverless applications).
- Conversational UX/UI design (chatbots) and Human-Agent-Interaction (HAI) is a plus.
- 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 embeddings, ANN/KNN, vector stores, database optimization, & performance tuning is a plus.
- Experience with LLM orchestration frameworks like Langchain, LlamaIndex, LangSmith, LangGraph, Google Agent Development Kit, is a plus.
- Experience with Generative AI Guardrails, responsible AI, adversarial attack mitigation, and red teaming is a plus.
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.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Number of Employees
5,001-10,000 employees
