Innodata Inc.

Build and optimize LLM post-training, fine-tuning, and evaluation systems that turn model assessment into measurable improvements. This role bridges ML research and production engineering while supporting internal R&D and customer-facing AI initiatives.

Lead the architecture of a secure federal data services platform, including its DataCard governance framework, storefront, orchestration, and write-back capabilities. This role is the program’s primary technical decision-maker below the Delivery Owner.

AI Solutions Engineer integrating AI-assisted annotation, synthetic data, computer vision, and ML workflows into a governed federal data platform. The role focuses on Python-based AI/ML configuration and integration above the infrastructure layer, with strong emphasis on model quality and auditability.

Build the backend and integration layer for a secure federal data services platform, connecting storefront services, Databricks, Duality, ServiceNow, and governance workflows. You’ll own reliable asynchronous APIs, write-back paths, and retry handling during an intensive 20-week delivery phase.

Build the training, post-training, and evaluation systems that improve large language and multimodal models for leading AI organizations. This hands-on role bridges ML research and production engineering across customer projects and internal R&D.

Technical Solutions Architect leading customer-facing engagements for LLM evaluation, fine-tuning, and post-training systems. The role translates complex AI goals into scalable solutions, pilots, and delivery programs for frontier labs and enterprise AI teams.