Software Engineer - Site Reliability Engineer
About the job
- At least 5 years of experience in site reliability engineering, DevOps, systems administration, or related roles.
- Experience managing complex infrastructure, troubleshooting production issues, and optimizing system performance in high-scale environments.
- Experience administering Linux or Unix systems and proficiency in scripting languages such as Python, Bash, or Go.
- Deep understanding of cloud platforms such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure, including services such as EC2, S3, Lambda, and Kubernetes.
- Experience with containerization and orchestration technologies such as Docker and Kubernetes.
- Proficiency with monitoring and observability tools such as Prometheus, Grafana, Datadog, Dynatrace, and the ELK Stack.
- Understanding of networking fundamentals including DNS, HTTP, TCP/IP, load balancing, and content delivery networks.
- Experience with continuous integration and continuous delivery tools such as Jenkins, GitLab CI, and CircleCI, as well as infrastructure automation.
- Familiarity with distributed systems and microservices architecture.
- Strong problem-solving and troubleshooting skills.
- Strong analytical skills, including the ability to identify Service Level Indicators and align efforts with availability and latency objectives.
- Ability to balance development and support responsibilities effectively.
- Strong interpersonal and communication skills, with the ability to collaborate across teams.
- Experience working on projects involving business segments.
- Must be a United States citizen.
- Design, implement, and maintain monitoring, alerting, and observability solutions to proactively detect and resolve issues before they affect end users.
- Lead troubleshooting efforts for complex production issues, provide root cause analysis, and implement preventative measures.
- Develop and maintain automation scripts, Bazel build systems, and infrastructure as code using tools such as Terraform, Ansible, or CloudFormation.
- Collaborate with software engineering teams on the design of new services and applications to ensure scalability, reliability, and resilience.
- Participate in on-call rotations to respond to platform emergencies, alerts, and escalations and maintain high service uptime.
- Analyze system performance and recommend optimizations for scalability, reliability, and efficiency.
- Implement and enforce best practices in deployment, monitoring, and incident management to improve system reliability and reduce downtime.
- Develop and maintain internal tools for complex operations, bug tracking, continuous integration and continuous delivery pipeline management, and cross-team communication.
- Conduct post-incident reviews and document software problems and solutions in a shared knowledge base.
- Assist with vulnerability management, system patching, and security measures that protect the integrity and availability of services.
About the company
Lovelace AI builds enterprise-scale context engines that turn massive streams of real-time data into usable knowledge graphs for autonomous agents. Its Elemental platform combines data ingestion, entity resolution, and graph construction in a single pipeline, enabling agent-driven workflows to perform complex investigations with speed and scale. The proprietary YottaGraph delivers real-time, real-world context with millisecond precision, so agents can understand how global information affects enterprise data. Compared with competitors, Lovelace handles trillions of data points at enterprise scale, offers an end-to-end pipeline, and targets agentic deployments with high accuracy and speed. The company's goal is to empower mission-critical decision-making by providing context-rich graphs that power autonomous agents across large organizations.
Company Size
11-50
Company Stage
N/A
Total Funding
N/A
Headquarters
Pittsburgh, Pennsylvania
Founded
2023
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Simplify's Take
What believers are saying
- August 2026 advisory board added Diane Greene and H.R. McMaster, boosting enterprise credibility.
- July 2026 benchmarks claimed Gemini Deep Research parity at under 1% cost.
- May 2026 stealth exit and seed backing from RRE Ventures accelerated commercial defense sales.
What critics are saying
- OpenAI and Google can bundle cheaper context into Gemini, crushing Lovelace pricing by 2027.
- Seed-stage Lovelace lacks disclosed revenue, making enterprise pilots fragile before renewals.
- Defense and bank customers demand flawless explainability; one failed answer kills procurement cycles.
What makes Lovelace AI unique
- Andrew Moore’s team sells context engines, not base models, for mission-critical workflows.
- YottaGraph links 60 million entities and trillions of facts with verifiable citations.
- Elemental targets regulated buyers needing millisecond queries, auditability, and firewall-contained data.
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Growth & Insights and Company News
6 month growth
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-3%Lovelace, a Pittsburgh-based context engine for enterprise AI agents, has launched its Elemental Core on Google Cloud Marketplace. Customers can buy it through their Google Cloud account and deploy it in their own Google environment. The containerised platform turns data in Google Cloud Storage buckets into knowledge graphs. Lovelace says it resolves entities at 10,000 a second with over 99.5% accuracy, scales to one trillion edges, and traces every output to its source. In benchmarks on research workloads, it cut token use by up to 100x. Purchases count toward a customer's existing Google Cloud commitment. "Giving an AI agent access to an organization's data is the easy part," said co-founder Toby Smith. "The harder part is making sure the context it works from is trustworthy and accurate.
Lovelace adds web pioneer R.V. Guha to its Strategic Advisory Board. Creator of RSS, RDF, and Schema.org brings decades of work making data usable by machines to Lovelace's growth in mission-critical AI PITTSBURGH, Oct. 6, 2026 /PRNewswire/ - Lovelace, the context engine for enterprise agents, today announced that R.V. Guha has joined its Strategic Advisory Board. Guha created RSS, RDF, and Schema.org, foundational technologies that shaped how information is structured and shared across the web. Guha has spent his career on the problem at the center of Lovelace's work: turning fragmented data into knowledge machines can reason over. During nearly two decades at Google, where he was named a Google Fellow, he founded Data Commons, a knowledge graph that unifies public data from many sources into a single resource. After serving as Technical Advisor to the CEO at OpenAI, he joined Microsoft, where he conceived and developed NLWeb, an open project that lets people and AI agents interact with websites conversationally. Earlier in his career he co-founded Epinions and held research and engineering roles at Apple, Netscape, and IBM. On the board, Guha will advise Lovelace on technical strategy and on how enterprises and AI agents can reliably access and reason over connected knowledge at scale. "Guha has shaped how the world structures information for longer than almost anyone," said Andrew Moore, co-founder and CEO of Lovelace. "RSS, Schema.org, and Data Commons each made data more usable by machines at the moment it mattered most. Enterprises are at that moment again with AI agents. They need context they can trust, connected across sources that were never designed to work together. Guha's perspective on how knowledge should be represented and shared will be invaluable as we bring Lovelace to more mission-critical environments." "For most of my career, I've worked on one question: how do you give machines a reliable understanding of the world?" said Guha. "AI agents have made that question urgent. Their answers are only as good as the context underneath them, and most enterprise data was never built to provide it. Lovelace is taking on that problem with real rigor and at real scale. That's work I want to be part of." Formed in August 2026, the Strategic Advisory Board guides Lovelace's growth, business strategy, and direction, with a focus on finance, defense, and other environments where the wrong decision can result in serious consequences. Guha joins founding members Diane Greene, co-founder and former CEO of VMware and former CEO of Google Cloud; Lieutenant General H.R. McMaster, U.S. Army (Ret.), former U.S. National Security Advisor; Philip Moyer, President and CEO of McGraw Hill and former CEO of Vimeo; and John M. Donovan, former CEO of AT&T Communications and a board director of Lockheed Martin and Palo Alto Networks. Lovelace provides AI systems with the context needed to reason accurately across fragmented and incomplete data. Its platform connects and resolves information into an evolving model of the people, organizations, assets, and relationships relevant to a mission, helping enterprises and government organizations deploy more accurate, efficient, and dependable AI. About Lovelace Lovelace is the only provider of enterprise-scale context engines capable of analyzing trillions of real-time data points to create knowledge graphs that are usable by autonomous agents at the speed, scale, and accuracy required for mission-critical analysis. Lovelace's context engine platform, Elemental, uniquely integrates data ingestion, entity resolution, and graph building into a single pipeline that empowers agentic deployments, exponentially amplifying the investigative power for complex queries. With its proprietary ground-breaking YottaGraph, Lovelace provides enterprises with real-time context, enabling agents to understand the impact of global intelligence on enterprise data for unmatched insights with millisecond precision. Founded in 2023 by Andrew Moore, former head of Google Cloud AI and dean of Carnegie Mellon's School of Computer Science, Lovelace currently works with some of the largest public and private enterprises in the world. SOURCE Lovelace AI Posted In:
Lovelace, a Pittsburgh-based AI startup, has formed a strategic advisory board to guide its expansion in mission-critical markets including financial services and national security. The board comprises four prominent figures: Diane Greene, co-founder and former CEO of VMware and former Google Cloud CEO; Lieutenant General H.R. McMaster (Ret.), former US National Security Advisor; Philip Moyer, president and CEO of McGraw Hill; and John M. Donovan, former CEO of AT&T Communications. The company provides enterprise-scale context engines combined with real-time world reference models for mission-critical applications. Its platform, Elemental, analyses trillions of real-time data points to create knowledge graphs for autonomous agents. Founded in 2023 by Andrew Moore, former head of Google Cloud AI, Lovelace currently serves major public and private enterprises globally.
Lovelace has released benchmark results showing that enterprises can generate AI-powered research equivalent to Google's Gemini Deep Research using open-source models running on their own hardware. The company's YottaGraph-context engine, paired with a locally-hosted Gemma 4 model, achieved comparable research quality whilst reducing inference costs from approximately $7 per report to about one penny in electricity costs. The benchmark evaluated 18 complex investment banking research scenarios, including competitive analysis and investment memoranda. Lovelace's locally hosted system delivered research quality statistically equivalent to Gemini Deep Research without cloud-based AI agents or external web search. The approach allows organisations to keep sensitive information behind their firewall whilst significantly reducing ongoing AI costs. Founded in 2023 by Andrew Moore, former head of Google Cloud AI, Lovelace provides enterprise-scale context engines for autonomous agents.
Ex-Google Cloud AI head building investigative agents for FIs. Andrew Moore bringing data prowess, AI practicality to fintech Lovelace. Reading Time: 3 mins read Andrew Moore, former head of Google Cloud AI, says building investigative AI agents for risk management workflows is like "solving a huge game of Sudoku, with literally millions of little facts, trying to find a pattern in it all." Moore's fintech startup, Lovelace, which exited stealth mode in late April, is building investigative AI agents that analyze vast data sets to help institutions fight financial crime, monitor and predict market changes, identify risk and bolster compliance operations, Moore told FinAi News. Moore's 14 years at Google helped shape his strategy for Lovelace through learning to maximize extensive data and apply advanced AI to tangible use cases, Moore said. "What I learned during that period was all the ins and outs of what it takes to do this translation of very theoretical, advanced research coming out of these brilliant, incredibly expensive research and engineering labs, and determining how to actually help systems run more efficiently," he said. For example, Moore's experience overseeing fraud prevention at Google translates to anti-money laundering initiatives in banking, he said. "Anti-money laundering investigations inevitably involve lots and lots of data points," he said. "You can't just look at one transaction... It's how that transaction fits in a massive pattern and things that change over time, whether that's geography, different financial instruments, different industries and so forth." Lovelace has raised an undisclosed amount in a seed funding round led by RRE Ventures, with other investors including Magarac Venture Partners, United States Innovative Technology and Carnegie Mellon University, according to a Lovelace spokesperson. Moore cannot yet name its banking partners due to non-disclosure agreements, but said early adopters comprise large and medium-sized institutions in the United States and United Kingdom. Context engine. While data quality is crucial for effective AI agents, successful deployment starts with extensive model training to establish a "knowledge graph" or "context engine," especially when dealing with millions of data points, Moore said. "When the agent starts out with its question, before it ever has to touch all that data in the background, you can formulate a really good plan for what data the agent is going to work with," Moore said. These knowledge graphs also help ensure explainability and auditability, which are a priority for FIs when vetting potential fintech partners, he said. "You have to have multiple independent lines of reasoning," he said. "When [agents] finish their work, they've always got to show this unbroken chain of bits of information they used as evidence... You have to actually show a high-quality statistical analysis, as well." Leaving tech giants. Moore joins others who have left tech giants to start fintechs, including: * Carmelle Cadet, left IBM after 10 years to start central banking infrastructure provider Emtech; * Twitter co-founder Jack Dorsey, left to start payments processing and merchant services platform Block, formerly called Square; and * David Marcus, left Meta to start payments company Lightspark.