Software Engineer, Architecture, Reliability, & Compute
As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, support end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and resilient cloud infrastructure for international government partners. You will take full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies, oversee the end-to-end health of the platform ensuring seamless integration between AI core and full-stack components, build automated systems to monitor model performance and data drift across geographically dispersed environments, manage the technical lifecycle within diverse regulatory frameworks, lead response for production issues in mission-critical environments ensuring rapid resolution and prevention, translate technical performance metrics into clear insights for senior international government officials, and partner with Engineering and ML teams to ensure field lessons influence future technical architecture and decisions.
Engineering Manager, Active Learning
The Engineering Manager role at Deepgram involves leading the design and implementation of internal data and ML training systems. Responsibilities include recruiting, hiring, training, and supporting top engineering talent to build a world-class team; transforming cross-functional visions into detailed project plans with clarity on commitments, risks, and timelines; defining and owning technical strategy to accelerate ML training pipelines; promoting a strong team engineering culture focused on rigorous engineering standards and continuous improvement; partnering with DataOps and Research teams to design and implement new services, features, or products end to end; and coaching and mentoring engineers to support personal growth while achieving ambitious team goals.
Research Engineer, Machine Learning Systems
The responsibilities include architecting and managing horizontally scalable systems to accelerate the end-to-end training lifecycle for Speech-to-Text (STT) and Text-to-Speech (TTS) models, focusing on optimized data preparation, high-throughput training pipelines, distributed infrastructure, and automated evaluation tooling. The role also involves designing and implementing internal UIs and tools to make ML systems and workflows accessible and transparent to non-technical stakeholders. Additionally, the position requires overseeing and managing training tooling, job orchestration, experiment tracking, and data storage.
Sr. Engineering Manager, Machine Learning
Lead a talented team of engineers focused on improving AKASA’s machine learning capabilities and delivering cutting-edge products. Supervise and directly contribute across all parts of the LLM stack, including model fine-tuning, inference, evaluation, and deployment. Develop infrastructure and tooling to improve the model development lifecycle. Oversee a high-performing team via hands-on contributions and coaching. Translate business requirements into technical designs that work within constraints such as latency, cost, performance, and uptime. Set the vision and direction for the team and attract top talent to join AKASA. Attend in-office co-working days every Wednesday as part of the local R&D team.
Engineering Manager, Go - Assist & Chat
Own the observability and lifecycle management of AI features across the organization. Build tools and infrastructure to enable teams to develop, monitor, and optimize LLM-powered features. Design and implement closed-loop evaluation pipelines that automatically validate prompt changes. Develop comprehensive metrics and dashboards to track LLM usage including cost per feature, token patterns, and latency. Create systems that tie user feedback to specific prompts and LLM calls. Establish best practices and processes for the full lifecycle of prompts, including development, testing, deployment, and monitoring. Collaborate with engineering teams across the organization to ensure they have the tools and visibility needed to build high-quality AI features.
Compliance Program Manager
Own the observability and lifecycle management of AI features across the organization. Build tools and infrastructure to enable teams to develop, monitor, and optimize LLM-powered features. Design and implement closed-loop evaluation pipelines that automatically validate prompt changes. Develop comprehensive metrics and dashboards to track LLM usage, including cost per feature, token patterns, and latency. Create systems that connect user feedback to specific prompts and LLM calls. Establish best practices and processes for the full lifecycle of prompts: development, testing, deployment, and monitoring. Collaborate with engineering teams across the organization to ensure they have the tools and visibility needed to build high-quality AI features.
Head of Internal Tools Engineering
The Head of Internal Tools Engineering is responsible for owning the end-to-end strategy and roadmap for all internal tools, platforms, and automation, treating internal technology as a product. They make strategic build-vs-buy decisions, map current and next-state process flows, and lead systems transformation for internal teams. They architect and maintain the full engineering lifecycle of internal platforms, build seamless API-first ecosystems integrating various internal systems, ensure system reliability and operational resilience, and design scalable, secure architectures using cloud-native principles and microservices. They lead AI strategy by integrating AI and LLMs into internal workflows and deploying intelligent automation tools. They reduce cognitive load for internal users by providing standardized workflows and self-service capabilities, measure platform success by adoption, satisfaction, and productivity impact, and build, lead, and mentor a high-performing engineering team. They cultivate a collaborative culture, provide technical mentorship, foster psychological safety, partner cross-functionally with leadership across departments, and align internal platform investments with company strategy while demonstrating measurable ROI.
Head of Internal Tools Engineering
The role involves architecting, building, and scaling the internal technology ecosystem to accelerate workforce productivity, eliminate operational friction, and provide a compounding infrastructure advantage by treating internal tools with product rigor and user-centricity. Responsibilities include owning the end-to-end strategy and roadmap for all internal tools, platforms, and automation; making strategic build-vs-buy decisions; mapping current and next-state process flows and leading systems transformation. The role requires architecting and maintaining the full engineering lifecycle of internal platforms, building API-first ecosystems integrating with various business systems, owning system reliability and operational resilience, and designing scalable, secure cloud-native architectures. The role leads AI adoption and automation integration into internal workflows, including deploying intelligent automation tools, evaluating AI-assisted troubleshooting, and driving continuous experimentation with prototypes. The person will reduce cognitive load for internal users by providing golden paths and standardized workflows, ensuring frictionless onboarding, and measuring platform success via adoption rates, user satisfaction, DORA metrics, and productivity impact. Team leadership duties include building, leading, and mentoring engineers and managers, fostering a collaborative culture rooted in ownership, speed, craftsmanship, and psychological safety. The role partners cross-functionally with various company leadership teams to translate business needs into a unified technical vision, aligning internal platform investments with company strategy and demonstrating measurable ROI.
Senior Analytics Engineer
Design and develop AI applications primarily in Python. Run evaluations to validate models and package solutions for Kubernetes, AWS, or adapt them to customer on-premises clusters. Lead discovery sessions, guide pilot projects, and ensure successful deployments. Collaborate mostly remotely with occasional on-site workshops. Monitor system performance and reliability. Add to the logging, billing and auth services. Build internal tooling to automate repetitive tasks. Provide feedback on patterns, pain points, and reusable modules to the core product team to influence the future direction of the AI platform.
Solutions Architect (Dallas)
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 various patterns, implement agent logic using modern frameworks (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 present recommendations, and partnering with Engagement Managers and Product/Engineering teams.
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