POSITION SUMMARY

KKR is hiring a Forward Deployed Engineer to help bring AI into the daily work of teams across the firm. This is a hands-on engineering role with a builder's mindset: you are an embedded extension of the engineering team that works directly with investment teams to understand how their work gets done, then design, build, and ship production AI systems that improve it.

Your “customers” are internal: colleagues across private equity, credit, infrastructure, real estate, insurance and capital markets. You will sit with these teams and observe their workflows firsthand. You will identify where fragmented data, repetitive tasks, and manual handoffs slow high-value work, and translate ambiguous business challenges into well-defined technical problems. You will then own the solution end to end, from architecture, code, data integration, to evaluation, deployment, and adoption, in close partnership with your direct engineering teammates, product, AI, data, security, and platform colleagues.

The best solutions you build will not stay one-off. You will convert successful implementations into reusable skills, tools, agents, and workflow components that scale across teams and strengthen the firm’s broader AI platform. If you are an engineer who wants your code to shape how a leading global investment firm works, this role offers rare scope and impact.

RESPONSIBILITIES

• Engage teams across KKR’s businesses to observe how work is delivered, going beyond documented requirements to understand information flows, decision points, and day-to-day realities.
• Identify high-value opportunities where AI can remove bottlenecks, connect fragmented data, eliminate repetitive work, or improve the speed and quality of decisions.
• Translate loosely defined business challenges into clear technical problem statements, solution architectures, and delivery plans.
• Design, build, and ship production-grade AI applications, agents, integrations, and workflow automations using modern languages such as Python or TypeScript.
• Connect structured and unstructured data across enterprise systems and make it reliably usable by AI-enabled applications through pipelines, retrieval systems, and well-designed interfaces.
• Create, test, and maintain reusable AI skills, tools, prompts, connectors, evaluation frameworks, and workflow components that can be shared across teams and business units.
• Establish rigorous evaluations and success metrics that measure accuracy, reliability, adoption, time saved, decision quality, risk reduction, and business impact.
• Move rapidly from discovery to prototype to production while upholding high standards for security, data protection, privacy, compliance, reliability, and maintainability.
• Decompose organic, often undocumented workflows into modular capabilities — data, decisions, actions, and controls — and determine where AI, deterministic software, or human judgment is the right tool.
• Drive adoption through documentation, training, direct user feedback, and close, ongoing partnership with the teams you serve.
• Convert successful high-touch implementations into repeatable engineering patterns and scalable platform capabilities, feeding insights into the firm’s broader AI roadmap.
• Challenge conventional practices constructively, helping teams reimagine workflows rather than simply automating existing steps.

HOW YOU'LL WORK WITH THE BUSINESS AND TECHNOLOGY TEAMS

Forward Deployed Engineers and technology teams own the engineering side of AI enablement for business partners: architecture, integration, data pipelines, security implementation, and production operations.

Paired with a business-embedded AI Solutions counterpart, you will be in the room together from the first discovery conversation, and you will stay engaged together through deployment testing with users, measuring results, and iterating. Your job is to make sure what gets built solves the right problem, fits how people actually work, meets the firm's control requirements, and gets used. Along the way, you will also work with product owners, data teams, information security, and corporate functions such as legal and compliance, tailoring your communication to each audience.

QUALIFICATIONS

• A strong software engineering foundation, with hands-on experience designing, building, and operating production systems — at least or equivalent demonstrated capability.
• Proficiency in Python, TypeScript, or comparable modern programming languages, with experience across APIs, cloud platforms, distributed systems, and enterprise integrations.
• Practical experience building applications with large language models — including agents, retrieval-augmented generation, tool use, and orchestration — and evaluating their behavior.
• Facility with data: pipelines, databases, retrieval systems, and unstructured content, and the judgment to make imperfect enterprise data usable.
• Genuine commercial curiosity and a strong desire to learn KKR’s businesses — how investments are evaluated, how decisions are made, and which outcomes matter.
• Comfort operating in ambiguity: the ability to take a vague problem, ask good questions, uncover needs users may not initially articulate, and converge on a concrete solution.
• The range to move fluidly between writing code, discussing architecture, observing workflows, and engaging senior business stakeholders.
• Act as a conductor across a diverse set of stakeholders — including business teams, business-aligned product and engineering, AI platform product and engineering, and data operations — to align priorities, resolve dependencies, and drive projects forward to successful delivery.
• A commitment to engineering quality — testing, observability, security, privacy, and responsible AI practices — even when moving quickly.
• Clear communication and a collaborative, low-ego working style, with the ability to give and receive direct feedback.
• Experience deploying AI or data-intensive systems in large enterprises or regulated environments.
• Exposure to private markets, financial services, or other domains with complex, judgment-heavy workflows.
• Experience partnering directly with senior business stakeholders to shape and deliver technical solutions.
• A track record of turning bespoke solutions into reusable platforms, frameworks, or internal products.
• Experience with evaluation frameworks, observability tooling, or governance practices for LLM applications.

WHAT SUCCESS LOOKS LIKE

• AI solutions you built are running in production and used every day by the teams they were designed for.
• Workflows you touched are measurably better — faster cycle times, higher-quality decisions, less manual effort, or lower operational risk.
• Capabilities you created — skills, tools, agents, connectors, and evaluation frameworks — are reused by other teams and business units.
• Business partners trust you as someone who understands their work and seek you out with their hardest problems.
• The systems you ship are secure, reliable, well-evaluated, and maintainable long after launch.
• Lessons from your deployments visibly shape the firm’s broader AI platform and roadmap.