AI Engineer

Overview: Guidepoint seeks an experienced AI Engineer as an integral member of the Toronto-based AI team. The Toronto Technology Hub serves as the base of our Data/AI/ML team, dedicated to building a modern data infrastructure for advanced analytics and the development of responsible AI. This strategic investment is integral to Guidepoint’s vision for the future, aiming to develop cutting-edge Generative AI and analytical capabilities that will underpin Guidepoint’s Next-Gen research enablement platform and data products. This role demands exceptional leadership and technical prowess to drive the development of next-generation research enablement platforms and AI-driven data products. You will develop and scale Generative AI-powered systems, including large language model (LLM) applications and research agents, while ensuring the integration of responsible AI and best-in-class MLOps. The AI/ML Engineer will be a primary contributor to building scalable AI/ML capabilities using Databricks and other state-of-the-art tools across all of Guidepoint’s products. Guidepoint’s Technology team thrives on problem-solving and creating happier users. As Guidepoint works to achieve its mission of making individuals, businesses, and the world smarter through personalized knowledge-sharing solutions, the engineering team is taking on challenges to improve our internal application architecture and create new AI-enabled products to optimize the seamless delivery of our services. This is a hybrid position based in Toronto. What You'll Do: Architect and Build Production Systems: Design, build, and operate scalable, low-latency backend services and APIs that serve Generative AI features, from retrieval-augmented generation (RAG) pipelines to complex agentic systems. Own the AI Application Lifecycle: Own the end-to-end lifecycle of AI-powered applications, including system design, development, deployment (CI/CD), monitoring, and optimization in production

Overview: Guidepoint is investing in applied AI across its research platform. We're building conversational AI systems that interact with our global expert network at scale — combining real-time voice, LLM-driven orchestration, and retrieval over large proprietary datasets. You'll be an early engineer on a new initiative: taking working prototypes to production and shaping the architecture as the product scope grows. This is a hands-on role with broad ownership, close to product and leadership. This role is hybrid in our Toronto Office What You'll Do: Real-time conversational AI: voice- and chat-based agents with low-latency requirements, built on modern telephony and real-time media infrastructure. LLM orchestration: agentic systems that pursue goals over multi-turn conversations, with hard behavioral guarantees enforced in code — not just prompts. Multi-channel session management: stateful user interactions that move seamlessly across channels. Retrieval and grounding: connecting LLM systems to large internal knowledge assets, with structured data flowing back. Evaluation infrastructure: measuring conversational quality against human baselines and gating rollouts on metrics, not vibes. Own services end-to-end: design, implementation, deployment (Kubernetes/Azure), and operations, including latency-sensitive real-time workloads. Design and tune LLM pipelines: context engineering, structured outputs, model selection, cost/latency tradeoffs across vendors. Build the eval loop and treat quality as a number: define metrics, run experiments, gate releases on them. Integrate third-party AI and voice vendors behind clean abstractions so providers stay swappable. Partner with product and compliance stakeholders to encode domain rules into the system. Set engineering patterns for a growing team: code review, observability, incident habits. What You Have: 6+ years of software engineering, with 2+ years building LLM-powered products in production (not notebooks): orchestratio

About the role There’s a wide gap between an agent that works in a demo and one that works across millions of live transactions. Closing it is the job. You’ll embed with the teams and customers who depend on AI — risk, fraud, collections, payments, support, developer experience — and design, build, and ship agentic systems into their production environments. You’ll also help build the platform underneath: Paytm’s AI inference platform (Pi) and the agentic runtime, orchestration, and tooling that lets agents reason, plan, use tools, and run multi-step workflows safely. What you’ll do Embed & deploy Tackle greenfield problems alongside internal teams and customers — scope ambiguous needs and build agents from scratch that fit how they actually work. Own deployments end-to-end: discovery, build, integration, activation, and the tuning that earns trust and adoption. Lead pilots and demos, drive adoption, and clear blockers before they stall a rollout. Build agentic systems Architect agentic systems — reasoning, planning, tool use, memory, multi-agent coordination — that run real workflows with guardrails. Build safe tool-use infrastructure across APIs, databases, and services, with permissioning, sandboxing, and human-in-the-loop. Ship SDKs, patterns, and reusable blueprints so internal teams build and deploy agents fast. Make it reliable Design and run rigorous evals: measure quality, catch regressions, and feed results back into the system. Build observability, tracing, and guardrails that prove agents are safe and keep them safe as models and data drift. Own the multi-model inference your agents depend on (text, voice, code, vision) — latency, throughput, and cost. Lead Set technical direction and standards for agentic systems; mentor engineers and partn

Overview: Guidepoint seeks an experienced Data/AI Engineer as an integral member of the Toronto-based AI team. The Toronto Technology Hub serves as the base of our Data/AI/ML team, dedicated to building a modern data infrastructure for advanced analytics and the development of responsible AI. This strategic investment is integral to Guidepoint’s vision for the future, aiming to develop cutting-edge Generative AI and analytical capabilities that will underpin Guidepoint’s Next-Gen research enablement platform and data products. This role demands exceptional leadership and technical prowess to drive the development of next-generation research enablement platforms and AI-driven data products. You will develop and scale Generative AI-powered systems, including large language model (LLM) applications and research agents, while ensuring the integration of responsible AI and best-in-class MLOps. The Senior AI/ML Engineer will be a primary contributor to building scalable AI/ML capabilities using Databricks and other state-of-the-art tools across all of Guidepoint’s products. Guidepoint’s Technology team thrives on problem-solving and creating happier users. As Guidepoint works to achieve its mission of making individuals, businesses, and the world smarter through personalized knowledge-sharing solutions, the engineering team is taking on challenges to improve our internal application architecture and create new AI-enabled products to optimize the seamless delivery of our services. This is a hybrid position based in Toronto. What You'll Do: Architect and Build Production Systems: Design, build, and operate scalable, low-latency backend services and APIs that serve Generative AI features, from retrieval-augmented generation (RAG) pipelines to complex agentic systems. Own the AI Application Lifecycle: Own the end-to-end lifecycle of AI-powered applications, including system design, development, deployment (CI/CD), monitoring, and optimization

At Vanta, our mission is to help businesses earn and prove trust. We believe that security should be monitored and verified continuously, and we empower companies to practice better security and prove it with ease. Vanta has a kind and talented team, and while some have prior security experience, many have been successful at Vanta without it. As a Senior Applied AI Engineer at Vanta, you will play a crucial role in shaping Vanta’s AI offerings, setting technical strategy, and leading projects that leverage AI to deliver smarter, faster outcomes for our customers. You'll be part of a team integrating AI into the Vanta product, working alongside a multidisciplinary group of product engineers, machine learning engineers, product managers, designers, and security and compliance experts to implement, scale, and maintain AI-enabled product experiences. In this role, you’ll build products that enable Vanta’s customers to leverage AI to accelerate their journey towards compliance, managing risk, and earning trust. Visit our Vanta Engineering Blog to learn more about what our team is working on! What you’ll do as an engineer working on Applied AI at Vanta: Work cross-functionally to design and implement AI-powered features to deliver customer value and integrate LLMs with Vanta’s existing products and systems. You’ll work with other product engineers across Vanta to understand how AI systems can accelerate product adoption at Vanta Instrument evaluations, guardrails, and monitoring, and review customer usage to continually improve quality Collaborate with AI Platform engineers shaping foundational AI systems and tooling that accelerate product teams Make pragmatic tradeoffs that consider business priorities, user experience, and a sustainable technical foundation Mentor engineers, champion good technical and product instincts, and model a collaborative, high-ownership engineering culture How to be successful in this role: At least 7 years of industry experience as a software

As an AI Security Engineer , you will work at the intersection of software engineering, cybersecurity, and artificial intelligence. You will design and build security tools, assess AI-powered applications for vulnerabilities, evaluate third-party and open-source AI technologies, and help establish secure development standards for AI systems. This is a hands-on engineering role for someone who enjoys building solutions, solving complex technical problems, and exploring emerging security risks across technologies such as Generative AI, RAG, and agentic systems. What You'll Do Design, build, and implement tools for AI security testing, penetration testing, red teaming, and continuous vulnerability assessment. Develop mock and test AI applications to evaluate internal and third-party AI security solutions. Identify and assess security risks within AI-enabled applications and workflows. Develop security requirements, standards, and best practices for securely building and deploying AI applications. Evaluate third-party vendors, open-source tools, libraries, and AI technologies from a security perspective. Integrate security testing and evaluation capabilities into CI/CD pipelines and development workflows. Build scalable security tooling and automation that can be adopted across different teams and environments. Troubleshoot applications and solve complex engineering and security problems. Partner with engineering teams, security professionals, customers, vendors, and business stakeholders to understand requirements and deliver practical solutions. Create clear technical documentation covering tools, features, security assessments, testing methodologies, and findings. What You'll Bring 5+ years of software development experience building and supporting production applications. Strong hands-on programming experience with Python. Experience working with RESTful APIs and structured data formats such as JSON, XML, CSV, and HTML. Experience with CI/CD pipelines and mod