Role Description We are looking for an Edge AI Engineer to design, optimize, and deploy machine learning models that run efficiently on resource-constrained edge devices, including mobile platforms, embedded systems, and specialized accelerators. The role requires deep expertise in model compression, quantization, and hardware-aware optimization, along with strong systems engineering skills to ship reliable AI capabilities outside the data center. The ideal candidate has shipped edge AI in production environments where compute, memory, energy, and connectivity constraints fundamentally shape the engineering trade-offs. Key Responsibilities - Design and implement edge AI solutions optimized for diverse hardware including mobile SoCs, NPUs, and embedded accelerators. - Apply quantization, pruning, distillation, and architectural optimization to fit models within edge constraints. - Tune model performance for latency, energy efficiency, and memory footprint on target hardware. - Build cross-platform inference runtimes leveraging frameworks such as TensorFlow Lite, ONNX Runtime, and Core ML. - Optimize models for specific accelerator backends including DSPs, NPUs, and mobile GPUs. - Implement on-device model update, versioning, and rollback workflows that allow safe staged rollouts to large device populations and rapid recovery if a model release behaves unexpectedly in the field. - Design hybrid edge-cloud architectures that gracefully degrade based on connectivity and device capability. - Build telemetry pipelines that respect privacy while enabling continuous improvement. - Collaborate with hardware, firmware, and product teams to align AI capabilities with device constraints. - Implement secure execution paths, model protection, and integrity verification on edge devices. - Develop benchmarking suites that characterize accuracy, latency, and energy trade-offs across devices. - Drive responsible AI considerations including on-device privacy and bias evaluation. - Maintain comprehensive, current technical documentation — including architecture diagrams, design decisions, configuration references, runbooks, and operational procedures — so that the system remains supportable, auditable, and easy to onboard new engineers onto over time. - Stay current with edge AI hardware and software developments, regularly review release notes and community discussions, and translate noteworthy advances into concrete recommendations and adoption proposals for the team. Qualifications - Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field. - Six or more years of experience in ML engineering, with significant work on edge or mobile AI. - Strong proficiency in Python and C++. - Hands-on experience with model compression, quantization, and pruning techniques. - Experience with at least one major edge inference framework. - Solid understanding of mobile and embedded hardware architectures. - Experience deploying ML models to production on mobile or embedded platforms. - Strong performance engineering and profiling skills. - Familiarity with on-device privacy and security considerations. - Strong communication and cross-functional collaboration skills. Preferred Qualifications - Experience with custom NPU or DSP toolchains. - Familiarity with federated learning or on-device personalization. - Exposure to safety-critical or industrial edge deployments. - Open-source contributions to edge AI frameworks. - Experience optimizing LLMs for on-device inference. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] or contact us at (908) 505-3899. Learn more about Bright Vision Technologies at www.bvteck.com .

• Build and own backend services in Python and Node.js, architect serverless compute layers, and develop the APIs that tie frontend experiences to the underlying data and logic. • Contribute to sophisticated workflow engines; state machines, asynchronous event pipelines, and reliable retry/failure patterns across distributed services. • Design and evolve RESTful interfaces, integrate third-party and internal systems, and leverage advanced NoSQL data modeling techniques (composite key strategies, transactional operations) to keep things fast and correct under load. • Monitor, debug, and continuously improve system observability and deployment reliability on cloud-native infrastructure. • Collaborate across time zones, contribute meaningfully in code reviews, take end-to-end ownership of features, and help shape how the team works, not just what it ships.

• Design, implement, and deploy AI/ML systems end-to-end, from prototypes to hardened production services • Build and maintain data pipelines, retrieval layers, and training/inference workflows that support LLM and other model types • Develop and evolve retrieval-augmented generation (RAG) setups, including chunking strategies, embedding selection, and vector search design • Implement and tune agentic / multi-step workflows that orchestrate tools, APIs, and models to complete complex tasks • Add observability around AI behavior: evaluations, logging, metrics, and guardrails to monitor quality, drift, and failures • Integrate models with existing application backends and APIs, and design clean interfaces for internal and external consumers • Optimize systems for performance and cost (token usage, caching strategies, routing between models, etc.) • Contribute to architecture decisions, code reviews, and technical strategy within your team

• Serves as a leading technical resource for AI tooling, operations, and integrations across the enterprise. • Architect and build end-to-end AI technical designs (including enterprise data pipelines, platform infrastructure, and systems integration) to ensure scalable, reliable deployment within the IT environment. • Oversee data readiness and pipeline development, ensuring high-quality data availability and robust platform capabilities to support AI initiatives at scale. • Support governance and risk management processes for AI, incorporating responsible AI practices and compliance measures (e.g., data privacy, bias mitigation) to ensure ethical, secure solutions. • Continually develops or improves all enterprise architectural domains of applications, data, network, and infrastructure. • Designs, implements, oversees, and continuously improves security, compliance, and availability. • Develops strategies and direction for applicable systems solutions including disaster recovery. • Assists with complex projects that require multiple technical disciplines serving as the escalation point of contact for technical projects from inception to delivery. • Serves as a strategic advisor to the organization on all AI technology matters.