AI Infrastructure Engineer - AI, Data & Platforms

KKR is a leading global investment firm that offers alternative asset management as well as capital markets and insurance solutions. KKR aims to generate attractive investment returns by following a patient and disciplined investment approach, employing world-class people, and supporting growth in its portfolio companies and communities. KKR sponsors investment funds that invest in private equity, credit and real assets and has strategic partners that manage hedge funds. KKR’s insurance subsidiaries offer retirement, life and reinsurance products under the management of Global Atlantic Financial Group. References to KKR’s investments may include the activities of its sponsored funds and insurance subsidiaries.

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 Infrastructure function sits within the Technology organization and is responsible for building and operating the model training, fine-tuning, and inference platforms that power KKR's enterprise AI applications across its global business.

KKR is looking for an AI Infrastructure Engineer to join our Technology team in Gurugram. This role is responsible for building the platform that enables KKR's AI engineers to move quickly from prototype to production owing to how models get trained, fine-tuned, deployed, and served at scale across the firm.

Reporting into the AI Infrastructure leadership team and working in close partnership with AI Engineering, Applied AI, and Cloud Platform teams, this role will design and operate the systems that sit underneath every AI use case at KKR. The role is suited to a pragmatic platform builder who is energized by the challenge of inference optimization across accelerators, distributed training and custom fine-tuning, and the unglamorous-but-critical work of making AI systems reliable, performant, and cost-efficient in production.

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

• →Design and operate the model training, fine-tuning, and inference infrastructure that supports KKR's enterprise AI applications.
• →Build deployment pipelines for LLMs and classical ML models across batch, real-time, and streaming workloads.
• →Drive inference optimization across accelerator types (GPU/TPU and equivalents) and support custom fine-tuning workflows.
• →Stand up evaluation, monitoring, alerting, and rollback systems for models running in production.
• →Partner closely with AI Engineering teams to remove infrastructure friction and accelerate the path from experimentation to deployment.
• →Establish best practices for model versioning, artifact management, reproducibility, and responsible AI deployment in a regulated financial services environment.
• →Contribute to the long-term AI platform roadmap, ensuring KKR's infrastructure scales with the firm's growing AI ambitions.

• 6+ years of experience in ML infrastructure, MLOps, or platform engineering with a clear AI focus.
• Strong Python skills, with deep familiarity with model serving frameworks (vLLM, Triton, Ray, TorchServe).
• Hands-on experience deploying and fine-tuning models in production — not solely orchestrating workflows built by others.
• Solid cloud fundamentals across AWS, GCP, or Azure, with practical experience in containers and Kubernetes.
• Working knowledge of the harder edges of AI infrastructure: inference optimization across accelerators (GPU/TPU), custom fine-tuning, distributed training, quantization, and autoscaling for inference workloads.
• Pragmatic engineering mindset — bias toward what ships and performs reliably in production.
• Strong communication skills, with the ability to partner effectively with AI engineers, application developers, and cloud platform teams.

• Experience supporting AI workloads in financial services or other regulated industries.
• Familiarity with model evaluation, observability, and responsible AI tooling in production environments.
• Exposure to cost optimization at scale across accelerator and cloud spend.
• Contributions to open-source ML infrastructure projects.
• Advanced degree in a relevant field (Computer Science, Engineering, or equivalent).