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As a Staff Software Engineer in the Foundations department, responsibilities include leading the design and implementation of highly scalable systems for the Managed AI offerings, driving the long-term technical roadmap for the Foundations team to support growth and evolving AI workloads, working cross-functionally with Cloud Engineering to align technical goals and solve integration challenges, leading by example through high-quality code contributions and mentoring Senior and Staff-level engineers, championing reliability, observability, and performance by identifying and resolving systemic bottlenecks, and staying current with AI infrastructure trends to ensure efficient and powerful tools are utilized.

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The AI Engineer will design and develop intelligent agents powered by large language models (LLMs) using tool calling, orchestration frameworks, and advanced context management to enable reasoning, planning, and autonomous decision-making across complex workflows. Responsibilities include working hands-on with modern agentic stacks such as LangGraph and Autogen, implementing asynchronous and streaming architectures, and ensuring production-grade observability to build scalable real-world AI systems.

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Lead and scale Horizon3.ai's Offensive Engineering organization, overseeing teams responsible for exploit development, offensive content, and attack automation within the NodeZero platform. Set clear technical and product direction for how NodeZero identifies, exploits, and validates vulnerabilities across large, complex environments. Partner with Product, Precision Defense, and Platform teams to define and deliver offensive capabilities that influence the roadmap and enhance customer outcomes. Drive execution from proof-of-concept through production to transform cutting-edge attack research into scalable, productized features. Stay hands-on to guide architectural decisions and evaluate exploit and automation approaches, mentoring technical leads in building resilient, modular systems. Build, mentor, and scale diverse teams of software engineers, exploit developers, and offensive researchers, fostering a culture of collaboration, creativity, and engineering excellence that bridges offensive and product software development. Collaborate across engineering, product, and GTM teams to align offensive innovation with business priorities and ensure delivery of impactful capabilities for customers. This role is central to the mission of delivering continuous, autonomous security testing at scale.

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Lead the design and development of low latency, scalable, and reliable model inference and serving stack for SSM foundation models. Manage and mentor a team of platform engineers maintaining a high technical bar and strong engineering culture. Work closely with research and product teams to translate research into products. Own the architecture and roadmap for model serving infrastructure, distributed systems, and data processing platforms. Build highly parallel, high quality data processing and evaluation infrastructure for foundation model training. Drive execution across ambiguous, zero-to-one engineering projects and platform initiatives. Establish best practices for reliability, observability, scalability, and performance across platform systems. Help recruit, interview, and build the engineering team in India. Have significant autonomy to shape the platform and impact how AI is applied across devices and applications.

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Responsibilities include designing and building agent architecture that is steerable, verifiable, conversational, and empathetic while future-proofing it as large language models evolve; developing retrieval methods to ground answers in customer knowledge bases and handle unclear or clarifying cases conversationally; measuring and empowering customer improvement of agent quality through evaluations; ensuring chat agents function effectively over the phone with lifelike conversations at low latency; creating simulation and benchmarking platforms to test AI agents against real-world scenarios; developing intuitive no-code content management tools to guide and test AI agents; adapting traditional software development methodologies to accommodate AI agents' non-deterministic behavior, natural language interactions, and reliance on large language models through Sierra's agent development lifecycle; and accelerating generative agent development using tools like Cursor and Claude Code to build self-improving systems based on interactions, feedback, and self-play.

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Build the core systems that power agents including the Agent SDK such as the orchestration engine, runtime, and primitives that define how agents reason, take actions, and interact with users and systems. Design the agentic loop to build agents that are steerable, verifiable, conversational, and adaptive. Improve retrieval and grounding systems to ensure agents provide accurate and trustworthy responses by effectively retrieving and using knowledge. Build evaluation systems by designing frameworks that allow measurement and improvement of agent quality over time.

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The Software Engineer in the Defence team is responsible for building and extending critical components of client deliverables across diverse software domains, delivering robust technical artifacts in both compiled and non-compiled languages to meet project goals, and implementing defined engineering patterns and practices tailored for the Defence sector. The role involves collaborating closely with Machine Learning Engineering and Data Science teams to integrate and refine technical solutions, applying rigorous software engineering best practices to enhance scalability and quality of codebases, and executing CI/CD processes while managing application deployments on Kubernetes and bare metal environments.

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The Software Engineer in the Defence team will build and extend critical components of client deliverables across diverse software domains, deliver robust technical artefacts in both compiled and non-compiled languages, implement defined engineering patterns and practices tailored for the Defence sector, collaborate closely with Machine Learning Engineering and Data Science teams to integrate and refine technical solutions, apply rigorous software engineering best practices to enhance scalability and quality of codebases, and execute CI/CD processes while managing application deployments on Kubernetes and bare metal environments.

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As a Senior Infrastructure Engineer at Bland, responsibilities include contributing to the design of scalable architecture by building distributed systems using Kubernetes that handle high-volume, real-time voice processing with strict latency and reliability requirements; building and supporting machine learning infrastructure including training pipelines and real-time inference serving across multiple regions; maintaining robust integrations with enterprise telephony systems, SIP trunks, and VoIP infrastructure; identifying architectural flaws and solving them; ensuring platform reliability through monitoring, alerting, and incident response systems to maintain enterprise-grade uptime; anticipating and solving scaling challenges related to exponential call volume growth; and implementing security best practices and compliance requirements for enterprise customers in regulated industries.

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Own the observability and lifecycle management of AI features across the organization. Build tools and infrastructure to enable teams to develop, monitor, and optimize large language model (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.