Sr GCP DevOps Engineer

About the position

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 Generative AI platform engineering team is responsible for building trustworthy GenAI platform, workbench tools and automation to help enterprise build and deploy GenAI Apps. We are looking for a GCP DevOps/GenAIOps engineer to help us build the foundational platform on GCP/Vertex AI. This role requires versatility and expertise across a wide range of skills. Someone with a diverse background/experience and an engineer at heart will fit into this role seamlessly. This role will have a Hybrid work schedule, with the expectation of working in an office (Columbus, OH, Chicago, IL, Hartford, CT, Frisco, TX or Charlotte, NC) 3 days a week (Tuesday through Thursday).

Responsibilities

• Design and build fault-tolerant infrastructure to support the Generative AI Apps (RAG, Summarization, Agent, etc.).
• Ensure code is delivered without vulnerabilities by enforcing engineering practices, code scanning, etc.
• Build and maintain IAC (Terraform/Deployment Manager), CICD (Jenkins) scripts, Cloud Build, uDeploy, & GitHub Actions.
• Partner with our shared service teams like Architecture, Cloud, Security, etc. to design and implement platform solutions.
• Collaborate with the Data Science team to develop and maintain GenAI Applications.
• Design and build the Data ingestion pipeline (ETL) for Embedding and Finetuning LLM Application.
• Create templates (Architecture As Code) implementing Ref architecture application's topology.
• Manage and deploy applications in Jenkins/Github Actions, ensuring robust CI/CD pipelines and automation.
• Build, package and deploy containers onto various container orchestration platform (managed Cloud Run or GKE).

Requirements

• Bachelor's degree in Computer Science, Computer Engineering, or a technical field.
• Proven experience with Google Cloud Platform (GCP).
• 6+ years of experience with Terraform.
• 3+ years of experience with Docker & Kubernetes (K8s).
• 2+ years of experience with HELM.
• Proven experience in working with other cloud providers such as AWS cloud is a plus.
• Programming experience with Python is a plus but not required.
• At least 4 years of experience designing and building data-intensive solutions using distributed system design and event-driven architecture.
• 8+ years building and shipping software and/or platform infrastructure solutions for enterprises.
• 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) on GCP stack (Cloud Functions + Cloud Run).
• Knowledge of distributed NoSQL 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.
• Experience with GCP BigQuery, Cloud Functions, AI Platform, API Gateway, GKE/Docker is must.
• 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.
• Strong proficiency in embeddings, ANN/KNN, vector stores, database optimization, & performance tuning.
• Experience with LLM orchestration frameworks like Langchain, LlamaIndex, LangSmith, LangGraph, etc.
• Experience with Generative AI Guardrails, responsible AI, adversarial attack mitigation, and red teaming is a plus.
• 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.

Nice-to-haves

• Experience with other cloud providers such as AWS cloud.
• Programming experience with Python.
• Conversational UX/UI design (chatbots) and Human-Agent-Interaction (HAI).
• Knowledge about customization techniques across various stages of the RAG pipeline, including model fine-tuning, retrieval re-ranking, HNSW, and product quantization.
• Experience with Generative AI Guardrails, responsible AI, adversarial attack mitigation, and red teaming.

Benefits

• Short-term or annual bonuses
• Long-term incentives
• On-the-spot recognition