Azure AI Jobs

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As a Senior Python Engineer, the role involves leading the development and deployment of advanced AI systems for diverse clients, designing, building, and deploying scalable, production-grade machine learning software and infrastructure that adhere to strict operational and ethical standards. Responsibilities include leading technical scoping and architectural decisions for high-impact machine learning systems, defining and implementing best practices and standards for deploying machine learning at scale, collaborating with engineers, data scientists, product managers, and commercial teams to solve critical client challenges, acting as a trusted technical advisor to clients by translating complex concepts into actionable strategies, and mentoring junior engineers while contributing to the team's engineering culture and technical depth.

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Building and deploying production-grade ML software, tools, and infrastructure; creating reusable, scalable solutions to accelerate the delivery of ML systems; collaborating with engineers, data scientists, and commercial leads to solve critical client challenges; leading technical scoping and architectural decisions to ensure project feasibility and impact; defining and implementing Faculty’s standards for deploying machine learning at scale; acting as a technical advisor to customers and partners by translating complex ML concepts for stakeholders.

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The Platform Engineer is responsible for building robust, secure, and scalable cloud infrastructure for AI and machine learning workflows. This includes partnering with technical and non-technical stakeholders from idea generation through implementation and shipping, enabling Machine Learning Engineers and Data Scientists by contributing to internal best practices, standards, and reusable code repositories, proactively identifying and recommending new ways customers can leverage cloud infrastructure to solve their challenges, creating and maintaining reusable, company-wide libraries and infrastructure-as-code, and researching and integrating the best open-source technologies to enhance Faculty's infrastructure capabilities.

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The Infrastructure Engineer is responsible for designing, building, and deploying robust, secure, and scalable cloud infrastructure for AI and machine learning workflows. They will work in a cross-functional team and partner with technical and non-technical stakeholders from the initial idea generation through to implementation and shipping. The role involves enabling Machine Learning Engineers and Data Scientists by contributing to internal best practices, standards, and reusable code repositories. The engineer will proactively identify and recommend new ways customers can leverage cloud infrastructure to address their key challenges, create and maintain reusable company-wide libraries and infrastructure-as-code, and research and integrate the best open-source technologies to enhance Faculty's infrastructure capabilities.

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As a Lead Engineer at Eloquent AI, you will lead the development of AI-powered full-stack applications while overseeing and mentoring other engineers. You will remain hands-on across the stack, take ownership of technical direction, code quality, and delivery standards. Responsibilities include designing and building full-stack applications that power AI-driven workflows for enterprise users, overseeing and reviewing the work of other engineers to ensure high-quality, production-ready code, providing technical guidance, architectural direction, and hands-on support where needed, developing high-performance front-end interfaces for AI agent control, monitoring, and visualization, building scalable backend services that support real-time AI interactions, knowledge retrieval, and automation, working closely with AI researchers and ML engineers to integrate LLMs, RAG, and automation into production-ready systems, establishing engineering best practices across testing, deployment, and performance optimisation, and continuously iterating and refining AI-driven products balancing speed with robustness.

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As a Staff DevOps Engineer at webAI, you will design and architect secure, scalable cloud and edge infrastructure for deploying AI workloads across multi-cloud and hybrid environments, build and maintain production-grade Infrastructure as Code managing over 100 resources with GitOps workflows and automated validation, design and operate production Kubernetes clusters optimized for AI/ML workloads with GPU support, implement secure CI/CD pipelines with integrated security controls and automated deployment workflows, lead MLOps infrastructure initiatives including model deployment pipelines and monitoring, design observability and monitoring systems with tools like Prometheus and Grafana aligned to performance indicators, implement security best practices including least-privilege access and automated compliance validation, lead incident response and reliability initiatives including on-call rotations and post-mortems, architect disaster recovery and business continuity strategies with automated backup and failover processes, develop reusable infrastructure modules to standardize deployment patterns, mentor engineers on cloud architecture and DevOps best practices, and drive technical documentation and knowledge sharing including runbooks and infrastructure standards.

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Design, implement, and maintain scalable, secure agent-driven services and systems that autonomously accomplish tasks using modern AI frameworks. Develop and enhance robust infrastructure and high-throughput APIs focusing on core agent capabilities such as memory, communication channels, skills, intelligent decision logic, security, and workflow management. Integrate agent capabilities with backend services, data stores, vector databases, search/retrieval systems, and external APIs. Collaborate with product managers, AI researchers, data engineers, and UX teams to translate high-level agent use cases into robust, production-ready software. Ensure reliability, monitoring, and observability for all agent components including metrics, logging, CI/CD, and fault tolerance. Contribute to architectural design decisions and participate in rigorous code reviews to uphold quality and maintainability.

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Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.

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The Solutions Architect is responsible for designing scalable, highly-available infrastructure for AI platform deployments including compute, storage, networking, security, enterprise integration patterns, Infrastructure as Code (Terraform, Helm), multi-region HA/DR strategies, and CI/CD pipelines. They design multi-agent systems using different patterns, implement agent logic with frameworks like langchain/langgraph, design evaluation frameworks, optimize prompts with A/B testing, and guide deployment and operations. The role involves leading technical maturity assessments, working directly with enterprise customers to understand requirements and provide recommendations, and partnering with Engagement Managers and Product/Engineering teams. Responsibilities combine software development, infrastructure/platform engineering, and customer-facing skills focusing on Kubernetes cluster design to multi-agent system architecture to solve real business problems.

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The AI Implementation Manager is responsible for the end-to-end delivery and stabilization of Ema's agentic AI solutions, spanning from design alignment through production rollout and steady state. This role involves ensuring solutions align with Ema’s agentic architecture and platform capabilities. The manager must develop a deep understanding of customer business processes and constraints to translate business workflows into feasible agentic AI workflows. They provide delivery-focused technical oversight, anticipating potential implementation issues such as integration, data quality, scale, and edge cases. The manager serves as the primary delivery contact for customer business and IT stakeholders and coordinates across multiple internal teams including Engineering, Product, Data, Infrastructure, and Value Engineering. They manage delivery under pressure by coaching stakeholders and teams during high-stress phases to reduce chaos. They communicate delivery progress, risks, and decisions clearly to all audiences, tracking success through adoption signals and outcome-adjacent metrics. Additionally, the role includes providing day-to-day delivery leadership and mentorship, promoting shared standards, clear ownership, and delivery discipline.