Azure AI Jobs

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Lead the design and implementation of a scalable, high-availability voice infrastructure that replaces legacy systems. Build and refine multi-threaded server frameworks capable of handling thousands of concurrent, real-time audio streams with minimal jitter and latency. Deploy robust ASR > LLM > TTS pipelines that process thousands of calls concurrently. Develop robust logic for handling media streams, ensuring seamless audio data flow between clients and machine learning models. Build advanced monitoring and load-testing tools specifically designed to simulate high-concurrency voice traffic. Partner with Speech Scientists and Research Engineers to integrate state-of-the-art models into a production-ready environment.

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Drive the architectural vision for the GenerativeAgent product by designing and building a highly scalable, multi-agent platform for real-time voice and text customer service experiences across various industries. Act as a technical authority and advisor for multiple engineering teams, develop system design and technical roadmaps, and define communication, state management, and orchestration patterns for multi-agent systems. Design and implement scalable, multi-tenant deployment architectures, own and define system-level SLOs/SLIs focusing on latency, cost-efficiency, and fault tolerance, identify systemic risks with proactive mitigation strategies, partner with Security and Compliance teams to meet regulatory and security requirements, lead post-incident analysis and improvements, and collaborate cross-functionally with Product, Customer Engineering, Site Reliability Engineering, TPMs, and Research to translate business requirements into system designs and productionize ML research. Mentor senior engineers and communicate complex technical concepts to both technical and non-technical stakeholders.

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The Solutions Engineer (AI/ML, Pre-Sales) will work closely with strategic customers to understand their data curation needs, business challenges, and technical requirements. The role involves leading end-to-end customer proofs of concept (PoCs) that connect data curation to training behavior and evaluation outcomes, including dataset analysis, training plan design, and interpreting results. They will partner with customer machine learning teams to map data and curation strategies, design and execute evaluation plans for base and post-trained models, select appropriate benchmarks and metrics, and run model evaluations. Additionally, the engineer will produce customer-ready evaluation reports detailing methodology, metrics, baselines, ablations (e.g., curated vs raw data), conclusions, and recommendations for productionization. They must communicate technical results effectively to both ML experts and executive stakeholders, explaining tradeoffs in compute, latency, and deployment cost. Collaboration with go-to-market, engineering, and research teams is essential to deliver compelling demos, align on requirements, and incorporate customer insights into model training and product strategies. The role also includes providing technical guidance, training, and documentation to enable prospects to confidently assess the solution.

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Assist in designing and implementing custom large language models (LLMs) and fine-tuning models for specific tasks. Build and experiment with agent libraries and workflow orchestration frameworks. Explore neo-cloud technologies, containerized environments, and virtualized infrastructure. Learn and apply security and privacy best practices in AI pipelines and deployments. Collaborate with the team to document, test, and optimize agent behaviors and models. Participate in knowledge sharing and mentorship sessions to gain exposure to AI, cloud, and security tradecraft.

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The Solution Architect is responsible for developing detailed and scalable architectural designs to address client needs using Articul8 products and third-party libraries and tools. They run pilot programs with customers to demonstrate the feasibility and advantages of proposed solutions, including introducing new product features or building prototypes. The role requires working closely with clients to understand business challenges and technical requirements through workshops, meetings, and presentations. They optimize solutions for performance, reliability, and cost-effectiveness, selecting appropriate instance types, auto-scaling configurations, and storage options. Ensuring solutions comply with security best practices and regulatory requirements is necessary, including implementing identity and access management, data encryption, and other security measures. The architect also creates comprehensive documentation and provides training on solution implementation and management. Collaboration with cross-functional teams such as Applied Research, Engineering, Quality Assurance, and Customer Success is required to incorporate innovation and maintain product leadership. Additionally, the role involves mentoring and guiding junior team members and helping to build a culture of rapid innovation.

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The Senior Solution Architect will guide customers through their entire AI journey from initial solution architecture and technical discovery in pre-sales to hands-on implementation and optimization post-sale. Responsibilities include deeply understanding customer business challenges and crafting AI prototypes on the Articul8 platform to address business objectives, leading technical workshops, hackathons, and training sessions to enable customers, collaborating with Sales, Product, and Engineering teams to position the platform and deliver solutions, overseeing installation, configuration, and scaling of the platform in customer environments with a focus on security, reliability, and performance, developing and implementing tailored workflow solutions, architecting and tuning Kubernetes-based environments on AWS, Azure, GCP, and on-premises, delivering enablement workshops and documentation for long-term customer autonomy, monitoring and refining deployments for cost-effectiveness, scalability, and resilience, and gathering customer feedback to influence product roadmap and enhancements.

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Design, develop, and deploy AI/ML models ranging from traditional ML regression algorithms to transformer-based architectures. Train, fine-tune, and optimize deep learning and LLM-based solutions. Engage with customers to understand their needs and transform them into actionable tasks for developing new functionalities within the Articul8 platform. Collaborate with researchers, software engineers, and product teams to integrate new AI capabilities into applications. Implement and evaluate state-of-the-art automated testing and metrics to improve model accuracy and efficiency. Optimize models for both cloud and on-premises environments to ensure low latency and high availability. Develop APIs and micro-services to serve AI models in production. Stay current with the latest AI models, research, and best practices. Ensure ethical AI practices, data privacy, and security compliance.

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Design and build robust RESTful and GraphQL APIs using Python, Go, or Rust. Architect scalable systems for real-time AI/ML-powered applications. Partner with ML engineers and frontend developers to ship intelligent, user-facing features. Ensure performance, security, and reliability across hybrid (cloud/on-prem) environments. Lead technical architecture and long-term backend strategy. Drive best practices for code quality, observability, and API design. Mentor engineers and help grow a high-performance, AI-augmented engineering culture. Take ownership of customer deployments and support production excellence.

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Design and deploy scalable architectures for manufacturing use cases including industrial operations such as SCADA/HMI, EDA, IoT, Machine Learning, AI, and advanced analytics. Lead technical workshops, prototype solutions, and oversee platform deployment and optimization. Collaborate with product and engineering teams to incorporate customer feedback into the product roadmap. Advise customers on Articul8 product adoption strategies focusing on differentiating capability, security, cost, and operational efficiency. Architect and tune Kubernetes-based and containerized infrastructure leveraging cloud platforms (AWS, Azure, GCP, or on-premises) to ensure infrastructure excellence. Develop reusable solution templates and document best practices. Partner with sales and business development to drive cloud adoption and align solutions with industry trends. Continuously monitor and improve deployed solutions for performance and scalability. Communicate technical concepts clearly to diverse audiences and advocate for customer needs internally. Stay current with emerging manufacturing and cloud technologies to inform strategy and support other industry verticals when required.

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Design, develop, test, deploy, maintain, and improve scalable, secure, and high-performance backend systems with a focus on high availability, low latency, and cost-effectiveness. Serve as the subject matter expert in infrastructure for designing new products and introducing new technology to existing product lines. Collaborate closely with engineering and research teams to integrate infrastructure components with product features, ensuring optimal system performance and user experience. Design event-driven architectures and develop APIs and microservices to support real-time processing and analytics. Ensure system reliability, performance, and scalability through monitoring, logging, and error handling mechanisms. Stay updated with emerging trends, technologies, and methodologies to enhance infrastructure capabilities. Participate in code reviews, contribute to open-source projects, and mentor junior engineers.