AI Engineer - AI, Data & Platforms

KKR's Gurugram office provides best in class services and solutions to our internal stakeholders and clients, drives organization wide process efficiency and transformation, and reflect KKR's global culture and values of teamwork and innovation. The office will contain multifunctional business capabilities and will be integral in furthering the growth and transformation of KKR.

TEAM OVERVIEW

KKR's Technology team operates as a strategic partner to the business, driving the tools, platforms, and capabilities that enable KKR's investment professionals and operational teams to work with greater speed, precision, and insight. The AI Engineering function sits within the Technology organization and is responsible for building the data foundations, applied AI systems, and platform infrastructure that power KKR's enterprise AI strategy — from foundational model infrastructure to production AI applications that create measurable business value.

POSITION SUMMARY

KKR is looking for KKR is looking for an AI Engineer to join our Technology team in Gurugram. This role sits at the intersection of data engineering and applied AI, responsible for translating ambiguous business problems into production AI systems that drive measurable value across KKR's investment and operational workflows. Reporting into the AI Engineering leadership team and working in close partnership with Applied AI, Technology Strategy, and business stakeholders across KKR's core verticals of private equity, infrastructure, real estate, and credit, this role will own the full delivery loop — from shaping the underlying data, to building LLM-based applications and ML models, to putting them in front of users. The role is suited to a hands-on builder who treats data as the real moat and is comfortable operating without a data science safety net.

We are operating in a 4-day in office, 1-day flexible work arrangement.

ROLES AND RESPONSIBILITIES

• Partner directly with business stakeholders across KKR's investment and operational teams to translate ambiguous problems into AI-driven solutions.
• Build and maintain the data pipelines, feature stores, and retrieval layers that power KKR's enterprise AI systems.
• Develop, evaluate, and iterate on LLM-based applications, agents, and ML models in production, with a strong focus on evaluation rigor and user impact.
• Own data quality, lineage, and observability for AI workloads, ensuring outputs meet the accuracy and reliability standards required in a financial services environment.
• Operate end-to-end AI use cases — scoping the problem, shaping the data, building the system, and shipping it — without relying on a separate data science function.
• Partner with Applied AI and Technology Strategy teams to adapt globally developed AI use cases into production-ready workflows for KKR's businesses.
• Apply strong engineering fundamentals — clean code, structured problem decomposition, and clear technical communication — to ensure AI systems move efficiently from concept to deployment.

QUALIFICATIONS

• 6+ years of experience across data engineering and applied ML/AI, with a track record of shipping production systems in complex environments.
• Strong Python skills, with deep fluency in SQL and the modern data stack (Airflow/Dagster, dbt, Spark, Snowflake/Databricks).
• Hands-on experience building with LLMs, including RAG, fine-tuning, evaluations, and agentic workflows.
• Demonstrated ability to take a vague business question, cut through ambiguity, and turn it into a shipped product.
• High signal-to-noise thinking — comfort making decisions and pushing forward without complete information.
• Grit and conviction; the ability to drive ideas through to delivery even when the path is unclear.
• Exceptional communication skills, with the ability to engage both technical and non-technical stakeholders.

PREFERRED QUALIFICATIONS

• Experience working with or across private equity, infrastructure, real estate, or broader financial services.
• Hands-on experience deploying LLM-based applications or ML models in an enterprise setting.
• Familiarity with AI evaluation frameworks, prompt engineering best practices, and agentic system design.
• Exposure to AI adoption patterns, change management, or digital transformation programs.
• Advanced degree in a relevant field (Computer Science, Engineering, or equivalent).