Computer Vision Engineer

Lead the design, build, and scale-out of applied AI systems embedded within core business workflows. The role focuses on operationalizing GenAI, enterprise agents, and decision-support systems, moving from experimentation to production-grade adoption with measurable business impact.

Responsibilities:

• Define and execute the applied AI roadmap aligned to enterprise workflows.
• Build and operationalize AI agents, copilots, and automation systems within production environments.
• Architect integrations between AI systems and core enterprise platforms.
• Drive outcomes such as automation, decision quality, turnaround time reduction, and operational efficiency.
• Establish scalable patterns for deploying and managing AI capabilities across teams.

Requirements:

• 7+ years building and deploying AI systems in production environments.
• Demonstrated ownership from problem framing to solution architecture deployment adoption.
• Experience working with cross-functional enterprise stakeholders (product, engineering, and operations).

Core Technical Expectations:

• Strong applied AI engineering experience using Python and modern AI tooling.
• Experience deploying AI systems via APIs and integrating them into enterprise stacks.
• Familiarity with document intelligence, conversational interfaces, and workflow automation.
• Production experience with cloud environments, CI/CD pipelines, and containerized services.
• Systems thinking across data pipelines, application layers, and user workflows.
• Deep Specialization Requirement (must have at least one).

Candidates must demonstrate deep, production-grade expertise in one of the following areas.

• Computer Vision: inspection workflows, document understanding, image/video analytics
• Voice Agents: conversational AI, call automation, speech-first workflows.
• Enterprise AI Agents: workflow agents, task orchestration, decisioning systems, human-in-the-loop operations.

GenAI / LLM Experience:

• Hands-on experience deploying LLM-powered applications in production.
• Prompt design, retrieval-based architectures, and agent orchestration patterns.
• Experience with both proprietary and open-weight model ecosystems.
• Practical understanding of reliability, evaluation, latency, and cost-performance trade-offs.

Platform and Integration Orientation:

• API-first mindset with experience embedding AI into enterprise products and internal systems.
• Experience building reusable AI components, services, or internal platforms.
• Ability to translate business workflows into AI-enabled system designs.

Domain Preference:

• Experience building AI systems for enterprise applications and/or regulated environments (e. g., financial services, healthcare, compliance-heavy operations, risk-driven workflows).
• Familiarity with operational constraints such as auditability, reliability, and human oversight in decision systems.