Location: Bangalore
About Evident Digital Pathology
Evident Scientific, formerly part of Olympus Corporation, is a global leader in life-science microscopy and optical instrumentation. Following the recent acquisition of Pramana, a pioneer in Autonomous Digital Imaging, Evident has combined its world-class optical heritage with Pramana’s software, robotics, and AI innovation to define the future of Digital Pathology.
Our young, high-performing engineering team has already taken the industry to its next node in autonomy—building the world’s most advanced whole-slide imaging platform, deployed at the Mayo Clinic to create the world’s largest digital pathology archive. Today, this technology is trusted by more than 30 top-tier medical institutions across the U.S. and Europe, powering Clinical workflows directly aiding in patient care.
As part of Evident, this team is now advancing to the next level of autonomy—moving from autonomous imaging to intelligent imaging, where scanners, data systems, and user interfaces are self-aware, adaptive, and capable of real-time AI inference at the edge.
Role Overview
This leadership role will define and drive the software architecture for Evident’s next-generation Autonomous Digital Pathology platform—spanning robotic scanner control, embedded compute, AI container orchestration, and cloud-scale data systems.
You will work with a proven engineering team to extend autonomy across the entire ecosystem—from robotic slide handling and imaging to inference, monitoring, data orchestration, and intelligent user experiences.
This is both a hands-on architectural and strategic leadership role, pivotal to Evident’s mission to deliver intelligent, self-optimizing digital pathology systems worldwide. This is a defining opportunity to architect and guide the development of next generation of intelligent, self-learning diagnostic imaging systems.
Key Responsibilities
1 | Platform & Systems Architecture
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Define and evolve the architecture for platform, integrating robotics, imaging, AI, and data pipelines.
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Establish standards for modularity, scalability, and interoperability.
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Drive the roadmap from automation toward intelligent autonomy.
2 | Robotics & System Autonomy
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Architect frameworks for robotic motion control, sensor fusion, and adaptive imaging that enable self-correcting, high-throughput scanning.
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Integrate real-time feedback loops that couple mechanical precision with AI-based decision-making.
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Ensure reliability, calibration, and deterministic performance across devices.
3 | Compute & AI Architecture
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Optimize CPU and GPU architectures for camera and motor controls, low-latency AI inference and real-time image analysis.
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Standardize compute frameworks (CUDA, TensorRT, ONNX Runtime, etc.) across scanner and workstation environments.
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Enable third-party AI model deployment through secure, containerized runtimes.
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Implement adaptive feedback systems allowing scanners to “think and respond” intelligently.
4 | Cloud, Data & Intelligence
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Architect the on-prem and cloud data infrastructure connecting scanners, metadata, AI inference, and analytics.
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Define APIs and synchronization layers between edge devices and cloud services.
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Ensure compliance with HIPAA, GDPR, and IVDR, while enabling scalable, hybrid deployments.
5 | Leadership & Team Development
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Mentor the engineering team, blending startup agility with Evident’s global engineering scale.
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Collaborate with R&D, Product, and Operations to align architecture with business goals.
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Represent Evident’s technology vision in executive, customer, and partner engagements.
Qualifications
Required
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10+ years in software engineering, with 5+ in architectural or senior technical leadership roles.
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Expertise in distributed systems, edge computing, and cloud-native architecture.
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Deep experience with robotics software frameworks (ROS/custom motion control) and real-time systems.
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Strong knowledge of CPU/GPU architectures and AI inference optimization.
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Proficiency in Linux, Docker/Kubernetes, networking, and database design.
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Track record of integrating AI/ML pipelines into production environments.
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Exceptional communication and team-building skills.
Preferred
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Experience in digital pathology, medical imaging, or automation systems.
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Familiarity with DICOM/PACS, AI inference frameworks.
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Exposure to FDA, HIPAA, GDPR, or IVDR compliance.
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Background in hardware–software co-optimization and vision-guided robotics.
