Lead AI Engineer

Job not on LinkedIn

4 days ago

Apply Now

Receive Emails with Similar Jobs

Beyond

WebsiteLinkedInAll Job Openings

Artificial Intelligence • Cloud • SaaS

Beyond is a company that provides a range of cloud services and solutions focused on enhancing data, artificial intelligence, and cloud initiatives. They are known for their expertise in Generative AI, Machine Learning, and MLOps, working with partners like Google Cloud to deliver high-quality solutions. Beyond offers services such as cloud implementation, operations support, and AI-driven workplace solutions. They also focus on improving customer experiences and creating innovative technology products. Beyond has a global presence, delivering solutions in regions including the US, UK, Ireland, and Europe, and they are a trusted Google Cloud partner.

51 - 200 employees

🤖 Artificial Intelligence

☁️ SaaS

📋 Description

• Lead Agent Architecture: Architect, design, and deploy scalable, multi-agent systems for high-traffic environments, leveraging frameworks such as Google's ADK (Agent Development Kit), LangGraph, CrewAI, or similar graph-based and role-based orchestration tools. • Implement High-Performance Pipelines: Design and build the underlying service architecture, utilizing Python or Go to ensure low-latency and high-concurrency for blended conversational and transactional flows. • Manage Agent Memory and Knowledge: Implement advanced short-term memory (session state) and long-term memory management, including integrating and maintaining Vector Data Stores (e.g., Google Cloud Vector Search) for efficient information retrieval (insert/extract). • Drive Retrieval Strategy: Design and implement robust RAG (Retrieval Augmented Generation) and RAGGraph strategies to ground agents in trusted enterprise knowledge, ensuring factual accuracy and reducing hallucination. • Tool Integration and Extensibility: Define and implement mechanisms to extend agent functionality by integrating with external APIs and services (Tools), including systems via MCP (Multi-Cloud Platform) servers or similar gateways. • Establish Reliability and Guardrails: Design, implement, and maintain observability, logging, and security guardrail frameworks to guarantee the correctness, safety, and compliance of agent behaviors in production. • Define Evaluation Frameworks: Create and industrialize automated evaluation frameworks that measure business outcomes (e.g., resolution time), technical reliability (latency, error handling), and the agent’s reasoning/tool-use correctness against defined rubrics. • Develop robust data pipelines: Build data ingestion, transformation, and export pipelines to create high-quality training datasets. • Automate Deployment: Design, implement, and maintain CI/CD pipelines to enable continuous integration and automated deployment of the agents, their correlated cloud components, and infrastructure-as-code configurations. • Champion Best Practices: Act as a technical leader, mentoring engineering peers and championing engineering best practices around system design, documentation, and continuous delivery within a cloud-native environment.

🎯 Requirements

• Experience: 8+ years in software, AI/ML, or systems engineering, with a minimum of 3 years directly designing and deploying LLM solutions or LLM-based, multi-agent systems at scale. • Architecture and Scale: Proven experience designing and operating high-throughput, distributed backend systems in cloud environments (GCP), utilizing Kubernetes, Docker, and service meshes. • LLM Orchestration Mastery: Deep hands-on experience with modern agent orchestration frameworks (LangGraph, Google ADK, CrewAI) and core AI techniques like ReAct (Reasoning and Acting), Plan-and-Execute, and prompt engineering for autonomous agents. • MLOps & DevOps: Demonstrable expertise in building and maintaining CI/CD pipelines (e.g., Jenkins, GitLab CI, Cloud Build) for deploying and versioning machine learning models and serverless components. • Data and Memory: Expert knowledge of data modeling and integrating vector stores (e.g., Pinecone, ElasticSearch, Vertex AI Vector Search) for high-performance retrieval and entity-aware long-term memory. • Cloud Ecosystem Focus: Strong hands-on experience with the Google Cloud Platform ecosystem, including Vertex AI (Generative AI Studio, Models), BigQuery, and Cloud Run/Functions for deployment. • Software Engineering: Mastery of Python and/or Go for production-grade development, including building robust APIs (REST/gRPC) and implementing CI/CD pipelines. • Leadership and Alignment: Proven ability to influence technical direction across multiple teams and align diverse stakeholders (ML, Product, Security) around a cohesive agentic vision.