Role Description Serious Development is a boutique healthcare strategy, product, and software engineering firm. We partner with healthcare organizations to solve complex operational challenges through thoughtfully designed custom software. We are actively seeking a full-stack AI/ML Software Engineer to help us build HealthTech apps and systems for our clients by contributing as a member of one of our agile engineering teams. Responsibilities include: - Implement scalable microservices that will work in concert together as applications. - Shape pieces of technical architecture and integrate our applications with ML & third-party technologies. - Enhance existing features and participate in code reviews. - Help ensure that the team is delivering high quality code and optimize existing systems. - Become an SME and owner of some of the services and systems that you helped implement. - Contribute your experience to make your team and the department stronger. Qualifications - Bachelor of Science degree in Computer Science, Computer Engineering, Software Engineering, or similar engineering major. - 4+ years of experience as an AI/ML Software Engineer with same/similar responsibilities (excludes any internship experience). - Experience engineering with Python. - Experience engineering in Linux and/or MacOS environments. - Experience engineering a SaaS product/platform. - Experience accurately logging all design, development, and consulting hours daily to prevent revenue leakage and ensure precise client invoicing. - Excellent written and verbal communication skills (English Level at least C1- Advanced). - Role based in Europe. - Must have valid work authorization and residency in Europe. Requirements - Operate as a self-sufficient, T-shaped, full-stack AI/ML engineer. - Recommend AI/ML-driven solutions and implement them. - Review technical architecture documentation for the project and ensure that the engineering team's deliverables are implemented accordingly. - Develop code that fulfills requirements specified by the business, technical architects, and clients. - Ensure that the code you deliver has an extremely high level of quality and extremely low potential for defects that will surface in the production environments. - Author documentation of your code that is useful for your team members and other members of Engineering. - Participate as a team-player that works together with your fellow team members to deliver commitments on time. - Dive into code as technical challenges arise to perform root-cause analyses and implement resolutions. - Develop proofs of concepts as needed. - Review code written by your team members to help catch issues before they surface after deployment. - Keep current with engineering best practices, design principles, technology, security, and compliance to apply that knowledge to all the responsibilities above. Benefits - Working proficiency of Portuguese, Spanish, or Ukrainian language skills could be helpful.

• Lead a team of Product Managers across AI Platform, Analytics, Portals, and Platform/Integrations • Set strategy and ladder to the 2026 GA calendar and 2028 vision • Own the AI Platform and agent strategy • Partner with engineering on AI architecture decisions • Define outcomes and articulate measurable customer and business outcomes

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.