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.

Experience
7-11 yrs