• Deploy and manage AI platforms supporting enterprise copilots, AI agents, and digital workers. • Implement and maintain Retrieval-Augmented Generation (RAG) infrastructure. • Deploy and manage vector databases and enterprise knowledge systems. • Develop and maintain integrations with RegScale APIs and other enterprise platforms. • Integrate AI services with Microsoft Copilot Studio, Power Platform, Azure AI Services, OpenAI, Anthropic, and related technologies. • Develop automation workflows using Power Automate and API-first architectures. • Design and maintain cloud-native infrastructure supporting AI and automation workloads. • Manage Kubernetes, Docker, CI/CD pipelines, and Infrastructure as Code. • Implement secure deployment pipelines for regulated environments. • Support development of compliance automation workflows for evidence collection, POA&M management, and continuous monitoring. • Monitor AI platform performance, observability, logging, and operational health.

Role Description We are looking for an experienced AI Automation Engineer to design, build, and scale automation solutions and AI-powered workflows across business operations. This is a highly hands-on role where you will work closely with internal stakeholders and external clients to identify process improvement opportunities, implement intelligent automation, and deploy AI agents that drive measurable operational efficiency. You will own projects end-to-end—from workflow discovery and process mapping to implementation, optimization, and ongoing enhancements. The ideal candidate combines operational thinking with expertise in no-code/low-code automation platforms and modern AI tools. Key Responsibilities - Process Analysis & Automation Strategy - Analyze and document existing business workflows across operations, sales, HR, customer support, and other functions. - Identify automation opportunities by evaluating process complexity, frequency, and business impact. - Develop and prioritize automation roadmaps focused on efficiency, scalability, and business value. - Workflow Automation Development - Design, build, test, and maintain automated workflows using platforms such as n8n, Zapier, Make, or similar no-code/low-code tools. - Integrate business systems including CRM platforms, databases, communication tools, and internal applications. - Implement automated notifications, approvals, routing mechanisms, and operational workflows. - System Integration & Data Flow Management - Connect multiple business platforms through APIs, webhooks, and automation frameworks. - Ensure seamless data synchronization and process orchestration across systems. - Develop scalable integrations that improve operational visibility and reduce manual effort. - AI Agent & Intelligence Solutions - Design and deploy AI-powered agents using platforms such as OpenAI, Claude, and other large language model technologies. - Build AI-driven research, analysis, and intelligence workflows. - Develop automated knowledge systems, data enrichment processes, and decision-support tools. - Create AI solutions for both internal teams and customer-facing use cases. - Scoring & Decision Frameworks - Build rule-based workflows and intelligent decision engines. - Implement lead scoring, prioritization models, routing logic, escalation mechanisms, and automated alerts. - Develop business rules that improve operational efficiency and customer engagement. - Client-Facing Solution Delivery - Collaborate directly with clients to understand operational challenges and business objectives. - Design and deliver customized automation and AI solutions tailored to client needs. - Present recommendations, demonstrate solutions, and support successful adoption. - Documentation & Enablement - Create comprehensive process documentation, workflow diagrams, and implementation guides. - Ensure internal teams and clients can effectively manage and utilize delivered solutions. - Establish best practices and reusable frameworks for future automation initiatives. Success Metrics - First 90 Days - Complete workflow analysis and process mapping across key business functions. - Deliver multiple quick-win automation initiatives. - Successfully deploy initial AI-powered automation solutions. - Within 6 Months - Establish a prioritized automation roadmap. - Deliver client-facing AI and automation projects end-to-end. - Create reusable documentation, templates, and operational playbooks. - Within 12 Months - Build a scalable automation and AI ecosystem supporting business growth. - Enable significant productivity improvements through intelligent automation. - Establish repeatable frameworks for automation and AI solution delivery. Qualifications - 3–5 years of hands-on experience building automation workflows using no-code/low-code platforms such as n8n, Zapier, Make, or similar tools. - Experience developing AI agents, AI workflows, or intelligent automation solutions using OpenAI, Claude, or comparable platforms. - Strong understanding of workflow design, process optimization, and operational efficiency. - Experience integrating multiple business systems and applications. - Proven ability to design rule-based workflows, scoring models, and decision automation frameworks. - Strong stakeholder management and communication skills. - Portfolio showcasing automation, AI agent, or workflow implementation projects. Preferred Qualifications - Familiarity with APIs, webhooks, JSON, and basic scripting. - Experience with data enrichment and intelligence platforms. - Background in Revenue Operations (RevOps), Sales Operations (SalesOps), Business Operations, or Process Improvement. - Experience working with CRM systems, marketing automation platforms, and business intelligence tools. - Exposure to AI-powered productivity tools and enterprise automation ecosystems. Key Skills - Must-Have Skills - No-Code Automation - Low-Code Development - AI Agents - Workflow Automation - n8n - Zapier - OpenAI - Claude - Process Mapping - Business Process Automation - Good-to-Have Skills - APIs & Webhooks - Data Enrichment - CRM Integrations - RevOps - SalesOps - Workflow Orchestration - Business Intelligence - Automation Strategy What Makes This Opportunity Unique - Own and shape automation and AI initiatives from strategy through implementation. - Work directly with business stakeholders and clients to solve real operational challenges. - Build impactful AI-driven systems that create measurable business outcomes. - Gain significant ownership, autonomy, and exposure to cutting-edge automation technologies. - Help define and scale the future of AI-powered operations within a fast-growing environment.

Title: Edge AI Engineer Location: usa Full Time Experienced Remote Job Description: Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge technologies to create scalable, secure, and user-friendly applications. As we continue to grow, we’re looking for a skilled Edge AI Engineer to join our dynamic team and contribute to our mission of transforming business processes through technology. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. Edge AI Engineer Job Title: Edge AI Engineer Location: 100% Remote (Continental United States) Position Type: In-house Bright Vision Technologies SOW engagement (no third-party client or vendor) Salary - 100 K - 150 k Experience: 6+ years Sponsorship: No new H1B sponsorship available. H1B transfers welcomed for qualified candidates. Employment Type: Full-time, direct W2 with Bright Vision Technologies (no C2C, no 1099, no third-party) Engagement: Long-term, multi-year, aligned to the Bright Vision SOW delivery roadmap Compensation: Competitive base salary commensurate with experience, plus benefits. Employment Terms & Visa Policy This is a 100% remote, full-time, direct W2 position with Bright Vision Technologies. This role is part of Bright Vision Technologies’ in-house Statement of Work (SOW) engagement. The client, end customer, and employer for this position is Bright Vision Technologies — there is no third-party client, vendor, or implementation partner involved. We do not engage in C2C, 1099, or third-party arrangements for this role BUT STRICTLY NO C2C/1099/3RD PARTY COMPANIES. ALL OUR ROLES ARE W2 AND NO 3RD PARTY BROKERING PLEASE. Candidates must be willing to work directly as a full-time W2 employee of Bright Vision Technologies and contribute to our in-house SOW deliverables. No new H1B sponsorship is available for this role. However, candidates who are currently on a valid H1B visa and require a transfer are welcome to apply. We will support H1B transfers for qualified candidates. For every role, a technical coding assessment is mandatory. Please apply only if you are confident in your technical abilities and hands-on experience. Job Summary 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. Required 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? We recognize that our people are our strength, and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Bright Vision Technologies is an Equal Opportunity Employer, including Disability/Veterans. Position offered by “No Fee Agency.” Equal Employment Opportunity (EEO) Statement Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall. BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.

• Lead the architecture, design, and engineering delivery of the organization's agentic AI platform • Drive the transformation of existing products, processes, and workflows into scalable agentic AI-enabled capabilities • Oversee end-to-end AI engineering operations • Partner with cross-functional stakeholders to define technical roadmaps • Build, mentor, and lead high-performing engineering teams