Full-Time
Data Center Deployment Ops Principal Engineer
DigitalOcean
1,001-5,000 employees
Cloud platform enabling rapid app deployment
Compensation Overview
$184k - $231k/yr
Seattle, WA, USA
Hybrid
 Operations & Logistics |
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 Software Engineering |
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- 10+ years of experience in data center architecture, infrastructure engineering, or a related field, with a track record of owning designs at scale.
- Deep expertise in Mechanical, Electrical, and Plumbing (MEP) systems for both leased colocation and build-to-suit data center environments.
- Strong working knowledge of cooling technologies across the spectrum—from air cooling to direct liquid cooling and rack-scale liquid systems—for high-density CPU and GPU deployments.
- Familiarity with AI/ML hardware roadmaps and the ability to translate evolving server, networking, and accelerator trends into future-proofed facility designs.
- Knowledge of emerging and non-traditional infrastructure approaches such as behind-the-meter (BTM) power and modular/prefabricated data center designs.
- Demonstrated ability to lead proof-of-concept initiatives and scale successful pilots into global deployment standards.
- Strong cross-functional collaboration skills, partnering with technical and business teams along with external vendors and partners.
- Excellent communication and leadership skills, including mentoring engineers and representing the company externally at conferences and through technical papers.
- Defining and owning DigitalOcean's reference architectures for next-generation Data Center environments, navigating shifts in technology, networking topologies, and AI/ML hardware roadmaps to keep facility designs future-proofed.
- Serving as DigitalOcean's primary technical stakeholder for Mechanical, Electrical, and Plumbing (MEP) systems across both leased colocation and build-to-suit environments, partnering with SMEs in the DC Ops organization.
- Provide leadership and guidance as a primary stakeholder in the new site selection process.
- Architecting end-to-end cooling designs with vendors and partner teams to support a wide variety of CPU, GPU, and other emerging server hardware.
- Architecting end-to-end power topologies with vendor teams to support diverse hardware across medium to ultra-high-density rack deployments, moving from 415VAC to 800VDC.
- Leading proof-of-concept (PoC) initiatives for emerging infrastructure technologies and transitioning successful pilots into global deployment standards.
- Guiding leadership through the implications of non-traditional data center facilities and emerging technologies such as behind-the-meter (BTM) power, to modular DC designs. Working with internal teams to weigh the business, commercial, and operational risks and tradeoffs.
- Mentor engineers at all levels and contribute to a culture of technical excellence, inclusivity, and impact.
- Represent DigitalOcean in the broader community attending conferences, contributing to papers and presentations.
DigitalOcean provides cloud computing infrastructure for developers, startups and SMBs to build, deploy, and scale applications using Droplets, managed databases, Kubernetes, object storage, and networking. It offers simple provisioning via a dashboard and APIs with fully managed services so teams avoid managing underlying infrastructure. It differentiates itself through a focus on simplicity, a strong developer community, open-source alignment, affordable pricing, and responsive support. The goal is to free developers from infrastructure chores so they can focus on coding and growing their business.
Company Size
1,001-5,000
Company Stage
IPO
Headquarters
New York City, New York
Founded
2012
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Simplify's Take
What believers are saying
- Q2 2026 revenue reached $281 million, rising 29% year over year.
- AI Customer ARR hit $234 million, up 212%, with inference services up 800%.
- Management added Richmond, Kansas City, and Memphis capacity against $894 million RPO.
What critics are saying
- July 23, 2026 stock issuance sold 12.5 million shares, diluting holders.
- AI growth depends on inference demand; any model-provider price war crushes margins.
- AWS, Microsoft, and Google can bundle cheaper inference, erasing DigitalOcean's differentiation.
What makes DigitalOcean unique
- DigitalOcean targets SMBs with simpler clouds, unlike AWS, Azure, and Google Cloud.
- Its AI-Native Cloud bundles inference, agents, and core infrastructure on one platform.
- Developer community and managed services reduce DevOps work for small teams.
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Benefits
Remote-first
Full health coverage
Wellness coverage
Flexible vacation time
Team-building & social events
401(k) plans
ESPP
Education support
Partner support
Employee giving
Growth & Insights and Company News
6 month growth
↑ 0%1 year growth
↑ 1%2 year growth
↑ 1%Add RAG to Hermes Agent with DigitalOcean Knowledge Bases. Advertisements DigitalOcean now offers a managed Knowledge Bases service that lets you wire retrieval-augmented generation into Hermes Agent without standing up your own vector store. The integration gives an LLM grounded answers from your documents, PDFs, or markdown files, and the setup takes about 20 minutes if you already have a DigitalOcean account. Disclosure. Some links in this post are affiliate links - Logicity earns a commission if you sign up, at no extra cost to you. Logicity only link products Logicity has used or actively recommend. Hermes Agent is an open-source Python framework for building tool-using LLM agents. Out of the box it supports function calling, memory, and multi-step reasoning. What it lacks is built-in retrieval. DigitalOcean's Knowledge Bases fills that gap by handling chunking, embedding, and vector search on their infrastructure. Advertisements A DigitalOcean account with billing enabled. Knowledge Bases is billed per GB stored and per query, with a free tier covering the first 1 GB and 10,000 queries per month. You also need Python 3.10 or later and the Hermes Agent library installed locally. Gather the documents you want the agent to reference. Supported formats include plain text, markdown, PDF, and HTML. Larger corpora work, but start small to validate the pipeline. Creating a Knowledge Base in the DigitalOcean console. * Log in to the DigitalOcean control panel and navigate to AI / ML > Knowledge Bases. * Click Create Knowledge Base. Name it something descriptive, like "product-docs" or "support-kb". * Select your data source: upload files directly, link a Spaces bucket, or connect a Git repository. * Choose your embedding model. DigitalOcean offers OpenAI's text-embedding-3-small by default; you can also bring your own endpoint. * Set chunking preferences. The defaults (512 tokens, 50-token overlap) work for most use cases. Adjust if your documents have long code blocks or tables. * Click Create. Indexing begins immediately; small corpora finish in under a minute. Once indexing completes, the console shows a status of "Ready" and reports the document count and total chunks. Connecting Hermes Agent to the Knowledge Base. DigitalOcean exposes each Knowledge Base through a REST endpoint. You authenticate with a personal access token scoped to read:knowledge_base. import os import requests from hermes_agent import Agent, Tool DO_API_TOKEN = os.getenv("DO_API_TOKEN") KB_ID = "your-knowledge-base-id" def search_kb(query: str, top_k: int = 5) -> list[dict]: """Retrieve relevant chunks from the DigitalOcean Knowledge Base.""" url = f"https://api.digitalocean.com/v2/knowledge_bases/{KB_ID}/query" headers = {"Authorization": f"Bearer {DO_API_TOKEN}"} payload = {"query": query, "top_k": top_k} resp = requests.post(url, json=payload, headers=headers) resp.raise_for_status return resp.json["results"] Wrap that function as a Hermes Tool so the agent can call it during reasoning: retrieval_tool = Tool( name="search_knowledge_base", description="Search internal documents for information relevant to the user's question.", function=search_kb,) agent = Agent( model="gpt-4o", tools=[retrieval_tool], system_prompt="You are a helpful assistant. Use search_knowledge_base before answering questions about company policies or product features.",) When a user asks a question the agent now retrieves context first, then generates a grounded response. Testing and iterating. Run the agent in debug mode to see which chunks it pulls. If answers are off-target, adjust top_k or revisit chunking. Overlapping chunks help when answers span paragraphs; shorter chunks work better for FAQ-style content. DigitalOcean's console includes a "Test Query" panel. Use it to spot-check retrieval quality before wiring the agent. Logicity's take. Managed vector stores are becoming table stakes. DigitalOcean's pricing (free up to 1 GB) undercuts Pinecone's starter tier and removes the ops burden of self-hosted Qdrant or Weaviate. For teams already on DigitalOcean infra, this is the path of least resistance. Alternatives worth comparing: Supabase pgvector (free tier, SQL interface) and Cloudflare Vectorize (tight Workers integration). Deploying the agent. Hermes Agent runs anywhere Python runs. For production, consider DigitalOcean App Platform or a Droplet behind a load balancer. Set your API token as a secret, enable HTTPS, and rate-limit the endpoint to avoid runaway query costs. If latency matters, deploy in the same region as your Knowledge Base. DigitalOcean currently hosts Knowledge Bases in NYC and SFO. Frequently asked questions. Does DigitalOcean charge per query? Yes. After the free 10,000 queries per month, pricing is $0.0001 per query. Heavy usage adds up; monitor via the billing dashboard. Can I use a local embedding model instead of OpenAI? Yes. Point the Knowledge Base to any OpenAI-compatible embedding endpoint, including a self-hosted model on a GPU Droplet. What happens if I update my source documents? Re-index from the console or trigger a sync via the API. DigitalOcean re-embeds changed files automatically. Is Hermes Agent required, or can I use LangChain? Any framework works. The Knowledge Base exposes a REST API; call it from LangChain, LlamaIndex, or plain Python. Need help implementing this? Logicity's consulting arm helps teams ship RAG pipelines on DigitalOcean, AWS, or hybrid infra. Reach out at [email protected]. Manaal Khan Tech & Innovation Writer Produced with AI assistance and reviewed by the Logicity editorial team. Learn more in its Editorial Policy.
Warren J. Adelman, a director at DigitalOcean Holdings, sold 4,200 shares of common stock for $521,000 on 7 August 2026, according to an SEC Form 4 filing. The weighted average sale price was $124.01 per share. Following the transaction, Adelman retained 67,433 shares directly, representing 94% of his reported equity position. The sale constitutes a modest reduction in his total direct exposure. DigitalOcean provides cloud computing infrastructure and developer tools to individual developers, startups, and small to mid-sized businesses globally. The company reported $1.0 billion in trailing twelve-month revenue and $235.2 million in net income, reflecting a net margin of 23.52%. As of 10 August 2026, DigitalOcean's stock price stood at $129.73, giving the company a market capitalisation of $13.6 billion.
DigitalOcean, a $14 billion cloud company, has seen its stock surge 275% over the past year, vastly outperforming Amazon, Microsoft, and Alphabet's average 31% return. The company targets small and midsized businesses with affordable AI cloud solutions through its AI-Native Cloud platform. The platform includes infrastructure across 20 data centers with Nvidia and AMD chips, plus access to AI models from OpenAI and Anthropic. Its inference router optimises prompts for cost and performance. At the end of Q2 2026, DigitalOcean reported $894 million in remaining performance obligations, up 12-fold year-over-year. This backlog indicates strong demand as customers await additional data center capacity. Whilst tech giants chase high-spending customers, DigitalOcean focuses on delivering simple, cost-effective AI services with personalised support.
Pranav Nambiar joins Rackspace Technology to scale sovereign AI Infrastructure. August 6, 2026 San Antonio, TX - August 6, 2026 - Rackspace Technology(R)(NASDAQ: RXT), a global enterprise AI infrastructure and solutions provider, today announced the appointment of Pranav Nambiar as Senior Vice President and General Manager, AI Infrastructure. Nambiar will lead the infrastructure, operating model, partnerships and go-to-market strategy behind Rackspace's AI infrastructure offerings, further strengthening the company's position as a governed, model-agnostic operator of AI in production for regulated and mission-critical enterprises. As enterprises move AI out of pilots and into production, demand has shifted from raw GPU capacity to governed compute: sovereign environments, compliance by design and operational discipline at scale. Nambiar's appointment accelerates Rackspace's investment in exactly that layer. Nambiar brings more than two decades of experience architecting multi-billion-dollar technology ecosystems and delivering transformative growth at Amazon Web Services, DigitalOcean, Google and Microsoft. At DigitalOcean, he served as Senior Vice President and General Manager of AI and Data Cloud, leading the company's strategic shift into a premier "AI Neo Cloud" and expanding production inference infrastructure for thousands of customers. "Enterprise AI has moved past the question of which model to use. The hard problem now is the infrastructure and operating discipline around the model: governed, sovereign, compliant and running in production at scale," said Gajen Kandiah, CEO of Rackspace Technology. "Pranav has spent his career building and scaling AI and cloud platforms at hyperscale, and he knows what it takes to run AI as a reliable operation rather than a pilot. His leadership will accelerate the infrastructure, operating model and partnerships behind sovereign, compliant and enterprise-ready AI for our customers. I am delighted to welcome him to Rackspace." Prior to his tenure at DigitalOcean, Nambiar led Google's generative AI and data services portfolio. At AWS, he led services including Amazon DynamoDB, Amazon Elasticsearch and Amazon SageMaker, and played a key role in shaping the company's data and AI platforms. Earlier in his career, Nambiar held multiple leadership positions at Microsoft, where he led digital strategies across its mobile, search and Windows businesses. He has advised C-suite executives and board members on enterprise AI, agentic innovation and autonomous systems for regulated industries. "Rackspace has something rare in this market: real infrastructure, deep operational experience and the trust of enterprises in the most demanding regulated sectors," said Nambiar. "The next phase of enterprise AI will be won in production, and I look forward to building the platform that gets customers there."
DigitalOcean Q2 earnings call highlights. August 5, 2026 Key points. * DigitalOcean's revenue rose 29% year over year to $281 million in Q2 2026, exceeding guidance. Management raised its full-year outlook to approximately 30.5% growth, with fourth-quarter growth expected to reach at least 35%. * Growth was driven by large customers and AI offerings: AI customer ARR surged 212% to $234 million, while ARR from customers spending at least $1 million increased 214%. Inference services grew nearly 800% year over year, and more than 6,000 customers have used the company's Inference Engine since its April launch. * DigitalOcean maintained strong profitability, reporting a 40% adjusted EBITDA margin and $61 million in adjusted free cash flow. The company is expanding data-center capacity, with approximately 155 megawatts committed, to meet demand that management said continues to exceed available supply. * MarketBeat previews top five stocks to own in September. DigitalOcean NYSE: DOCN reported second-quarter 2026 revenue of $281 million, up 29% from a year earlier and above the high end of its guidance, as growth accelerated among its largest customers and AI-focused offerings gained adoption. Chief Executive Officer Paddy Srinivasan said the company added a record $93 million in annual recurring revenue during the quarter, nearly triple the incremental ARR reported in the year-earlier period. The company also raised its full-year outlook, citing demand that it said continues to exceed available capacity. "We delivered 29% year-over-year revenue growth while continuing to have strong profitability," Srinivasan said. He added that DigitalOcean expects roughly 30% revenue growth for the full year and at least 35% growth in the fourth quarter. Large customers and AI revenue drive growth. DigitalOcean said ARR from customers spending at least $100,000 annually rose 98% year over year. ARR from customers spending at least $500,000 increased 160%, while ARR from customers spending $1 million or more climbed 214%. The company's largest customer cohort represented 23% of total ARR in the second quarter, compared with 9% a year earlier. AI customer ARR reached $234 million, rising 212% year over year, according to Chief Financial Officer Matt Steinfort. DigitalOcean said 85% of AI customer ARR came from inference services and Core Cloud products rather than Bare Metal offerings. Inference services grew nearly 800% year over year and accounted for more than 70% of total AI customer ARR, Srinivasan said. The company's remaining performance obligations rose to $894 million, more than 12 times the prior-year level, with an average duration of 3.7 years. Steinfort said those commitments were secured from a range of customers and that the company's top 25 customers accounted for 20% of ARR during the quarter. DigitalOcean also said it would no longer emphasize net dollar retention as a key metric. NDR reached 102% in the quarter, a three-year high, but Steinfort said the measure has become less representative of the company's business as growth increasingly comes from its biggest customers and newer AI customers. Inference Engine expands AI platform adoption. DigitalOcean launched its Inference Engine in late April, offering managed serverless inference and related technologies. Srinivasan said more than 6,000 customers have used the service since launch, with customer count growing by nearly 60% on average each month. Token volume increased 30-fold over the past 60 days, he said. Discover more Self-Help & Motivational ETF Screener Tool Stock Split Calculator Open-weight models accounted for about 15% of token volume shortly after the launch and had grown to nearly 75% by the time of the call. The company said it now offers more than 75 open- and closed-source models through a single endpoint and has completed 14 day-zero model launches since April. Srinivasan said DigitalOcean is seeing a shift from customers seeking the greatest possible token consumption toward optimizing the quality, latency and cost of AI workloads. He described the company's Inference Engine as a production runtime that integrates functions including routing, model evaluations, batch inference, prompt caching and server-side tools for AI agents. The company said its Inference Router, which adjusts requests across open and frontier models based on quality, latency and cost, had nearly 1,400 active customers. DigitalOcean also highlighted its launch-partner status for Kimi K3, stating that the model brought more than 400 net new customers in its first week on the platform. Core Cloud attachments and capacity plans. Management said it is seeking to build an "AI-Native Cloud" platform that connects inference, agents, data products and core infrastructure. More than half of new AI customers added year to date had attached a Core Cloud product, Srinivasan said. Among AI customers with at least $100,000 in ARR, roughly 70% had attached a Core Cloud product in the second quarter. The company cited customers and ecosystem relationships including OpenCode, Daytona, Vercel and OpenRouter. DigitalOcean said it serves more than 20 billion tokens per day through OpenRouter, up more than 330% over the prior 60 days. DigitalOcean launched data centers in Richmond during the first quarter and Kansas City during the second quarter, both ahead of schedule, management said. The company remains on track to open its Memphis data center in the second half of 2026. It also secured about 20 megawatts of additional capacity expected to come online in late 2027 and 2028. Total committed capacity is now approximately 155 megawatts, with the majority expected to be online by the end of 2027. Management said the company has 15 megawatts remaining to bring online this year. During the quarter, DigitalOcean increased list prices on several GPU fleets by approximately 30%. Steinfort said the effect on second-quarter incremental ARR was modest, while pricing actions were included in the company's outlook for the remainder of 2026. Profitability, balance sheet and outlook. Adjusted EBITDA was $114 million, representing a 40% margin. GAAP operating income was $29 million, or a 10% margin, while adjusted operating income was $67 million, or a 24% margin. Non-GAAP diluted earnings per share were $0.45, and adjusted free cash flow totaled $61 million. Trailing 12-month adjusted free cash flow was $175 million, equal to 17% of revenue. For the full year, DigitalOcean expects adjusted free cash flow margin of 11% to 13%. In July, the company retired approximately $472 million of its 0% convertible senior notes due in 2030. Steinfort said the transaction reduced leverage with minimal cash use and effectively no dilution because the underlying shares had already been reflected in diluted share calculations. * Third-quarter revenue is projected at $304 million to $307 million, representing 32% to 34% year-over-year growth. * Third-quarter adjusted EBITDA margin is expected to be 38% to 39%. * Third-quarter non-GAAP diluted EPS is forecast at $0.28 to $0.30. * Full-year revenue is expected to be $1.17 billion to $1.18 billion, representing approximately 30.5% growth. * Full-year adjusted EBITDA margin is projected at about 39%, with non-GAAP diluted EPS of $1.35 to $1.40. Management did not provide formal 2027 guidance but reiterated greater confidence in its previous expectation for revenue growth of more than 50% next year. Steinfort said the timing of future data-center capacity additions remains an important variable in determining the company's 2027 results. About DigitalOcean (NYSE:DOCN). DigitalOcean Holdings, Inc is a cloud infrastructure provider that focuses on simplicity, performance and developer experience. The company offers a range of cloud services designed to help software developers, startups and small- to medium-sized businesses deploy, manage and scale applications. Its flagship offering, Droplets, provides virtual private servers that can be configured with various CPU, memory and storage options. In addition to compute instances, DigitalOcean's platform includes managed Kubernetes, scalable object and block storage, managed databases, load balancers and networking capabilities such as Virtual Private Cloud (VPC) and Floating IPs. Founded in 2011 and headquartered in New York City, DigitalOcean was created with the goal of making cloud computing more accessible to individual developers and smaller teams. This instant news alert was generated by narrative science technology and financial data from MarketBeat in order to provide readers with the fastest reporting and unbiased coverage. Please send any questions or comments about this story to [email protected]. Continue following MarketBeat Before you consider DigitalOcean, you'll want to hear this. MarketBeat keeps track of Wall Street's top-rated and best performing research analysts and the stocks they recommend to their clients on a daily basis. MarketBeat has identified the five stocks that top analysts are quietly whispering to their clients to buy now before the broader market catches on... and DigitalOcean wasn't on the list. 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