About Genesis Computing

Genesis Computing AI builds an agentic data engineering platform centered on Eve — an AI agent that autonomously executes complex data workflows inside enterprise cloud environments (AWS, Snowflake, Databricks, Azure). We're a seed-stage team of ~20, founded by early Snowflake employees and led by seasoned enterprise operators. Our clients include hedge funds, financial services firms, and large pharmaceuticals. We move fast, ship constantly, and build with the same AI-native tools we sell.

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The Role

The Forward Deployed Engineer is the bridge between Genesis's product and the messy reality of our customer’s enterprise data environments. You'll embed directly with our highest-value clients — on-site or virtually — to deploy Genesis, design blueprints for their specific workflows, unblock adoption, and feed what you learn straight back to the core engineering team.

This isn't a traditional solutions or support role. You'll architect real integrations, and operate with the autonomy of a founding engineer — except your context is the client's environment, in addition to ours. You'll work across Snowflake, AWS, Databricks, and whatever else the client runs, configuring Genesis to handle multi-week data missions.

You'll become a deep expert in the Genesis platform — its capabilities, its edge cases, and its current limitations. We're early-stage, and that means you'll regularly encounter gaps between what clients need and what the product can do today. That's not a bug in this role — it's the point. Your job is to discover those limitations in the field, find creative solutions to keep clients moving, and feed precise, actionable feedback to engineering so we can close the gaps fast. We ship new versions quickly, and what you surface this week can be in production next week.  

You will also have access to the Genesis source code and the latest coding tools such as Claude Code, Cursor, etc. and a nearly-unlimited token budget to use in your work, so you will be able to field-test actual solutions where applicable. This isn’t a traditional software engineering role, but you should be highly technical and comfortable building real solutions when needed.

You're the person who sits inside the client's domain, understands their data landscape in the first week, and has Genesis driving real production value within weeks.. When something breaks or doesn't fit, you fix it — and you make sure the engineering team knows why it broke so it doesn't happen again.

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What You'll Do

Client delivery. Own end-to-end deployment of Genesis at enterprise accounts. Configure the platform inside client environments, design and customize blueprints, integrate with their existing data infrastructure, and get them to value fast.

Technical problem-solving on the ground. Debug issues in real-time during client engagements. Work across the stack — Docker, AWS, Snowflake, Python, APIs — to unblock customers quickly, owning immediate fixes while partnering with core engineering on root causes and product improvements.

Product feedback loop. You are the tightest signal we have from the field. Translate client friction into concrete engineering input — whether that's a Linear ticket, a blueprint pattern that should be productized, a prototype enhancement to Genesis or solution built using Claude Code or Cursor, or an architecture constraint the core team needs to hear about. As an early-stage product, rapid iteration is how we win — your field insights directly drive what we build and how fast we ship it.

Demo and pre-sales engineering. Support the sales team with technical demos tailored to prospect environments. Build POCs (we've done Investor360 chatbots, data pipeline automations, analytics dashboards) that show Genesis's capabilities in context.

Client relationship and trust. Become the technical point of contact that enterprise clients rely on. Companies need to trust the person configuring autonomous agents inside their infrastructure — you earn that trust through competence and follow-through.

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What We're Looking For

10+ years in a technical client-facing role — solutions engineering, forward deployed engineering, technical consulting, or a senior engineer who regularly interfaced with customers. You've deployed software inside someone else's environment and dealt with everything that entails.

Strong data engineering fundamentals. Snowflake, AWS, SQL, Python, and ideally some exposure to Databricks, Airflow, or dbt. You understand how enterprise data stacks actually work in practice, not just in architecture diagrams. This includes Data Engineering as well as Analytical workloads as you'll be looking at company's front-to-back challenges from ingestion to transformations to normalization to deriving value via BI and other analytical tools.

Full-stack versatility. You're comfortable with Docker, CI/CD, cloud infrastructure, APIs, and frontend work when the situation calls for it. You don't identify as a specialist — you identify as someone who solves the problem in front of them.

AI-native workflow. You use Cursor, Claude Code, or similar tools daily, with a nearly unlimited token budget. You know which models are good at what. You've internalized that modern engineering is about orchestrating AI tools with strong oversight, not hand-coding everything.

Enterprise communication skills. You can talk to a portfolio manager at a hedge fund and a data engineer at a pharma company in the same week. You translate technical complexity into business confidence.

Comfort with early-stage ambiguity. You've worked with products that weren't fully baked yet and you thrived — you know how to set client expectations honestly, work around limitations creatively, and channel frustration into product improvement rather than finger-pointing.

Bias toward action and ownership. At a 20-person startup, you’re expected to move problems forward without waiting — identifying issues, driving solutions, documenting what you learn, and sharing it with the team.

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Nice to Have

• Experience with LLM-powered or agent-based systems in production
• Familiarity with enterprise auth patterns (Auth0, SSO, RBAC)
• Background in large enterprise data environments
• Experience building and running POCs or technical pilots for enterprise sales cycles

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Why Genesis

Enterprise AI at the frontier — You'll deploy autonomous data agents inside real production environments at hedge funds and Fortune 500 companies. This isn't demo-ware.

Direct product influence — What you learn in the field shapes what we build. The feedback loop is measured in days, not quarters.

Exceptional team — Founders took Snowflake from early days to IPO. Head of engineering spent 10 years as a Managing Director at Goldman Sachs. Small team, high bar, no bureaucracy.

Career-defining skills — The intersection of enterprise delivery and AI-native engineering is where the industry is heading. You'll build that skillset here before most people know it exists.