Ai Agent Engineer Jobs

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. CVS Health is seeking an SDET Manager to lead Quality Engineering and software delivery transformation across the Medicare Sales & Acquisition ecosystem. This role is critical to modernizing how we build, test, and deliver software, including embedding AI-driven capabilities into the SDLC. You will translate business workflows (Sales, Enrollment, Broker, and Telesales) into scalable, automated testing solutions that improve speed, quality, and operational efficiency. This is a hands-on technical leadership role responsible for driving test automation strategy, enabling intelligent SDLC pipelines, and building a high-performing engineering team aligned to enterprise goals. This role will also help shape the team's transition toward AWS-native test infrastructure and agent-orchestrated cycles, positioning the organization to scale automation alongside delivery volume in future years. Key Responsibilities Lead the design and implementation of AI-augmented Quality Engineering solutions — including agentic test generation, self-healing frameworks, and AI-assisted defect analysis — advancing the SDLC toward an AI-driven Automated Delivery Lifecycle (ADLC) Drive the migration of automation frameworks and test data management onto AWS-native services (e.g., Bedrock for model orchestration, Lambda/Step Functions for event-driven test pipelines), enabling

What we're building Mutiny is the self-improving AI infrastructure for GTM teams to execute faster and close more revenue. Our ambition is to do for revenue velocity what Cursor and Claude Code did for engineering velocity. With Mutiny, everyone in sales and marketing gets a bench of GTM athletes that handle any work across their revenue motion and learn from what's actually moved their deals. In April we re-launched the product as an agent-first platform. Anthropic showcased us as a leader in AI GTM. MRR is growing more than 70% month-over-month, with customers like Uber, Rippling, and Snowflake. We're backed by Sequoia, YC, and Insight, and we're building a generational company. The opportunity Most CS roles are graded on how few problems the customer has. You'll be graded on how much they grow. As a growth strategist at Mutiny, you'll own a book of managed accounts and become the reason teams at customers like Rippling, Uber, and Snowflake get more out of AI than their peers. CS isn't a support function here. It's how we turn early traction into the customer base that defines a category. You'll build the playbooks as you go and shape what customer success looks like at an AI-native company from the ground up. This role is in person in New York City, five days a week. What you'll own Your book. Onboarding, adoption, retention, and expansion across your managed accounts. Every customer outcome runs through you. Customer expertise. Become the expert on how each customer's business works and where Mutiny fits in. Translate that into "aha" moments that move their roadmap. The AI motion. Teach customers how to use our agent-first platform to create assets that actually move the needle for their sales and marketing teams. Their team uses Mutiny because you made it obvious how. EBRs that move things. Run business reviews that tell a real story and drive action. Follow-ups that are crisp and lead somewhere. Renewals and expansion. Build the case six months before it comes

As a Research Scientist on our team, you will partner with Research Engineers, working on fundamental research problems and collaborating with Datadog's product and engineering teams to translate research advances into products. Building on our track record of AI-powered solutions (e.g., Bits AI , Bits Evolve , and our time series foundation model ), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security. We are focused on two research areas: World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents. Trained Agents for Observability -- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost. What You'll Do: Conduct research in generative AI and machine learning, building specialized foundation models and trained agents for observability Train multimodal models on large-scale, diverse telemetry data (metrics, logs, traces, topology, events) using distributed training infrastructure Design and build simulated environments and RL training loops for on-policy agent training and evaluation Collaborate with cross-functional teams (Product, Engineering) to integrate capabilities like multimodal world modeling and autonomous agents into Datadog's products Stay at the forefront of foundation models, world models, and RL-based agent research Contribute to r

As a Research Scientist on our team, you will partner with Research Engineers, working on fundamental research problems and collaborating with Datadog's product and engineering teams to translate research advances into products. Building on our track record of AI-powered solutions (e.g., Bits AI , Bits Evolve , and our time series foundation model ), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security. We are focused on two research areas: World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents. Trained Agents for Observability -- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost. What You'll Do: Conduct research in generative AI and machine learning, building specialized foundation models and trained agents for observability Train multimodal models on large-scale, diverse telemetry data (metrics, logs, traces, topology, events) using distributed training infrastructure Design and build simulated environments and RL training loops for on-policy agent training and evaluation Collaborate with cross-functional teams (Product, Engineering) to integrate capabilities like multimodal world modeling and autonomous agents into Datadog's products Stay at the forefront of foundation models, world models, and RL-based agent research Contribute to r

We are a global team of innovators and pioneers dedicated to shaping the future of observability. At New Relic, we build an intelligent platform that empowers companies to thrive in an AI-first world by giving them unparalleled insight into their complex systems. As we continue to expand our global footprint, we're looking for passionate people to join our mission. If you're ready to help the world's best companies optimize their digital applications, we invite you to explore a career with us! Your opportunity AI is a top strategic priority for New Relic, and the Bengaluru design team is at the center of it. The team works across three closely related product clusters: autonomous incident response (SRE Agent, Autopilot, and Intelligent RCA), the intelligence and platform layer that powers them (Ground Truth, Agentic Platform, and New Relic AI), and AIOps for event correlation and incident management. These products share a common design challenge: users need to trust systems that act autonomously, and building that trust through good design is genuinely hard work. This is an on-site role in Bengaluru. Your designers are there, and many of your engineering and product partners are too. Being present — in standups, reviews, and the quick conversations before a decision gets made — is part of how you'll build the relationships that make design effective. You'll also collaborate with design, product, and engineering partners in the US and Spain, so operating across time zones and communicating well in writing are part of the job. You'll manage a small team of designers and own the quality of the work across these products. The design questions here don't have established answers — how do you make an autonomous system legible? How do you build user trust in AI-generated root cause analysis? How do you design a handoff from machine decision to human judgment? If you're already working in AI product design, or actively building toward it, and you want to lead a team

What we're building Mutiny is the self-improving AI infrastructure for GTM teams to execute faster and close more revenue. Our ambition is to do for revenue velocity what Cursor and Claude Code did for engineering velocity. With Mutiny, everyone in sales and marketing gets a bench of GTM athletes that handle any work across their revenue motion and learn from what's actually moved their deals. In April we re-launched the product as an agent-first platform. Anthropic showcased us as a leader in AI GTM. MRR is growing more than 70% month-over-month, with customers like Uber, Rippling, and Snowflake. We're backed by Sequoia, YC, and Insight, and we're building a generational company. The opportunity Most post-sales leaders inherit a process. You'll build it. As our AI strategist lead, you'll own Mutiny's managed account book and lead the technical solutions function that backs up our most complex customers. The teams running Mutiny (Rippling, Uber, Snowflake, Zendesk, and Gusto) are betting hard on AI, and they need post-sales partners who move their roadmap forward, not just their renewal date. You'll set the bar for what great support looks like, build the playbooks that let CS scale, and step into people management as we hire AI strategists underneath you. This role is in person in New York City. The teams running Mutiny (Rippling, Uber, Snowflake, Zendesk, and Gusto) are betting hard on AI, and they need post-sales partners who move their roadmap forward, not just their renewal date. They don't need a partner who tracks their renewal date; they need one who moves their roadmap forward and shows them what's possible with the product before they ask. You'll set the bar for what great looks like, build the playbooks that let CS scale, and grow into people management as we hire AI Strategists underneath you. This role is in person in New York City. What you'll own The managed book. Onboarding, adoption, retention, and expansion across every managed account at Mutiny. T

Become a part of our caring community Help shape practical, responsible AI solutions that improve healthcare experiences and outcomes. Humana’s Enterprise AI organization develops safe, scalable AI solutions across our Insurance and CenterWell businesses. We bring together product managers, data scientists, engineers, policy experts, and business leaders to apply emerging technology to meaningful healthcare challenges. As Associate Director of Applied AI, you will lead teams that design, build, and deploy enterprise AI solutions, with a focus on generative AI and intelligent agents. You will connect technical strategy to business needs, guide responsible delivery in a regulated environment, and help teams turn promising ideas into measurable outcomes for members, patients, and associates. Key Responsibilities Lead and mentor teams developing production-ready AI solutions that improve healthcare delivery, member experiences, and business operations. Help define and execute the roadmap for applied AI initiatives in alignment with enterprise priorities and business needs. Guide the evaluation and adoption of machine learning, generative AI, large language models, multimodal models, and intelligent agent technologies. Oversee scalable APIs, frameworks, data pipelines, retrieval-augmented generation solutions, and agent orchestration capabilities. Partner with product, data science, engineering, architecture, security, and business teams to translate requirements into reliable solutions. Establish standards for AI evaluation, obse

Job Overview: We are looking for a Senior GenAI Developer to design, build, and productionize agentic AI systems—LLM-powered agents that can plan, use tools, orchestrate workflows, and operate reliably under enterprise constraints. You will own key parts of the agent architecture (planning, tool use, memory, evaluation, safety/guardrails, and observability) and deliver end-to-end solutions across RAG, function/tool calling, multi-agent coordination, and scalable deployment. Key Responsibilities Design and implement agentic systems: single-agent and multi-agent architectures (planner/executor, supervisor-worker, routing, reflection, critique, task decomposition). Build robust tool-using agents: function calling, tool schemas, tool authorization, retries, rate limiting, and sandboxing. Implement RAG + memory patterns: retrieval strategies, hybrid search, context assembly, long-term memory, and grounding/citation behaviors. Develop workflow orchestration for agent execution (state machines/graphs), concurrency controls, and deterministic execution where possible. Productionize GenAI services: APIs, background jobs, streaming responses, caching, and cost/latency optimization. Establish agent evaluation: golden sets, simulation-based evals, LLM-as-judge with mitigations, task success metrics, regression testing. Build observability and safety: tracing, token/tool telemetry, anomaly detection, prompt injection defenses, data leakage prevention, policy enforcement. Collaborate with product, security, and platform teams to deliver enterprise-ready solutions and integrate with internal systems (data, identity, workflow). Mentor engineers, set coding standards, and contribute to architecture reviews and technical roadmaps. Required Qualifications 6+ years software engineering experience; 2+ years building LLM/GenAI systems in production. Strong programming skills in Python (required) and/or TypeScript/Node.js. H

Scale Labs, Research Scientist — AI Controls and Monitoring As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities. Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision. As a Research Scientist focused on AI Controls and Monitoring, you will design methods, systems, and experiments to ensure that advanced AI models and agents remain aligned with intended goals, even in high-stakes or adversarial environments. For example, you might: Develop monitoring techniques and observability methods that track AI behavior in real time to identify and flag deviations, emergent capabilities, or anomalous outputs; Research mechanisms for layered control, including fail-safes, oversight protocols, and intervention methods that can halt or redirect AI systems when risks are detected; Design red-team simulations to probe weaknesses in oversight and control mechanisms, and build mitigations to close identified gaps; Collaborate with policymakers, engineers, and other researchers to establish standards and benchmarks for AI monitoring and escalation. Ideally you’d have: Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance. Practical experience conducting technical research collaboratively. You should be

Our mission and customers: We are creating the freedom for SMEs to succeed by delivering Europe's leading finance workspace with banking at its core, augmented by financial tools. We are proud to be rated 4.8 on Trustpilot, based on 55,000+ reviews. Our culture puts customer satisfaction at the core of what we do, as proven by our Net Promoter Score of 75 (more about our culture here). Our journey: Founded in 2017 by Alexandre and Steve, Qonto has grown to 1,600+ Qontoers serving over 600,000+ customers across 8 European countries. We have been profitable since 2023, and we are just getting started. Our beliefs: We hire for skills and potential. With 80+ nationalities, 45% women, of which 56% of women in our leadership team, diversity isn't a program; It's who we are. We've built a discrimination-free hiring process because the best teams are built on merit. AI at Qonto: AI is deeply embedded in how we work (here) - Every Qontoer gets unlimited access to the best AI tools. We want people who experiment without waiting for permission, push AI beyond the obvious, know when to trust it, and when to question it. ------------------------------------------------------------------------------------------------------ Join us as Product Designer in the AI Lab to design AI agents end-to-end — from discovery to delivery — and accelerate Qonto's AI roadmap alongside a team of ~12 engineers, a Product Designer, a PM, and a PMM building on top of one of the most advanced in-house agent platforms in European fintech. You will report to Sophie Cornay, Head of the AI Lab, and work within a fast-moving team of ~15 people shipping AI agents at high cadence. ➡️ What you'll do Design end-to-end experiences for AI agents (from concept to shipped UI/UX) across Qonto. Turn ambiguous problems into clear product flows through discovery, prototyping, and iteration. Run user research (interviews, tests) and translate insights into actionable design decisions. Partner daily with engineers a

We are looking for a Lead, Data Development, AI Platform to lead the team building and operating the technical platform behind Hootsuite's Analytics MCP. This includes routing infrastructure, orchestration services, agent infrastructure, and data pipelines that enable reliable AI-driven analysis at scale. You will set technical direction for the team, strengthen engineering practices, and translate target architecture and integration standards into secure, production-grade systems that Data Analytics & AI teams can confidently build on. This is a hands-on technical leadership role. You will stay close to the code while owning delivery outcomes, engineering quality, and the team's culture of craft and accountability. You will develop strong, well-reasoned recommendations on how the platform and orchestration architecture should evolve, seek approval at the Senior Manager and Director level, and then guide the team through disciplined execution. You will also partner closely with AI Context & Integration and AI Data Architecture to ensure the platform, context layer, semantic layer, and downstream agent workflows operate as one coherent system. WHAT YOU’LL DO: Lead the design and delivery of the orchestration layer that connects the Analytics MCP platform across its most complex surfaces, including query execution across schemas and models, federated data access between the data warehouse and external source systems, and multi-step agent workflows for cross-functional business processes. Develop clear technical recommendations for platform and orchestration architecture evolution, align those recommendations with Senior Manager and Director-level direction, and guide the team through disciplined execution within the approved architecture. Own the operational reliability, scalability, quality, and observability standards for core Analytics MCP components, including routing and agent infrastructure. Guide the team to build and operate these systems to prod

We are seeking an experienced Senior Generative AI Developer to help drive the design, development, and integration of state-of-the-art Generative AI and agentic AI solutions across our enterprise Controls Technology platform. You will collaborate with cross-functional teams, contribute deep technical expertise in context engineering, retrieval systems, knowledge graphs, and multi-agent orchestration, and play a key role in delivering scalable, grounded AI solutions to enhance automation and operational efficiency. This role centers on architecting robust applications and agent systems on top of pre-trained and hosted foundation models — not on training or fine-tuning models. Key Responsibilities Collaborate with AI architects, leads, and stakeholders to design and implement generative and agentic AI solutions that address business challenges. Architect advanced context engineering strategies — context layering, chaining, compression, pruning/offloading, and memory management — to maximize reliability, provenance, and token efficiency in production. Design and implement advanced generative AI methods, including sophisticated prompt engineering and Retrieval-Augmented Generation (RAG) . Build and optimize RAG systems , including hybrid search, multi-vector retrieval, and re-ranking pipelines. Design and implement knowledge graphs and Graph RAG architectures to enable multi-hop reasoning, explainability, and traceable, grounded responses for high-value business domains. Architect agentic workflows and multi-agent systems using Google Agent Development Kit (ADK) and comparable frameworks (LangGraph, Microsoft Agent Framework, CrewAI), applying orchestration patterns such as supervisor/worker, hierarchical, and peer-to-peer. Design robust agent harnesses — governance, constraints, feedback loops, state/session management, and

Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including frontier model training, enterprise adoption, defense applications, and autonomous vehicles. Our mission is to develop reliable AI systems for the world’s most important decisions. We’re looking for an AI Product Manager to own the Finance vertical within our Agents Data & Reinforcement Learning Environments team. In this role, you’ll own both the development of RL environments (the realistic, high-fidelity simulations of financial software and workflows that labs use to train and evaluate agents) and the “data as a product” strategy that powers them. You’ll understand where AI is being used in the Finance industry, decide what financial tasks are worth modeling, how to source and structure the underlying data, and how to turn deep domain knowledge into a defensible product. The ideal candidate has lived inside the Finance industry, and is able to pair that domain understanding with a sense for AI research and current agent capabilities in Finance workflows. You’ll translate that expertise into environments and datasets that teach AI agents to perform real financial work, and you’ll be the domain expert Scale’s most important customers and their leading researchers turn to. A strong entrepreneurial & go-to-market mindset will be necessary. What You’ll Do Own the Finance AI roadmap & data strategy: Set product direction for the Finance agents training stack and the data strategy behind it. Establish a vision for where AI is continuing to transform the Finance industry (including investment banking, private equity, public markets, corporate finance, FP&A, etc), driving execution across engineering, operations, and go-to-market teams. Build partnerships with research teams at frontier labs: Work directly with researchers at leading AI labs to understand where their Finance agentic capabilities fall short and shape new product lines and competit

About Us: Paytm is India's leading mobile payments and financial services distribution company. Pioneer of the mobile QR payments revolution in India, Paytm builds technologies that help small businesses with payments and commerce. Paytm's mission is to serve half a billion Indians and bring them to the mainstream economy with the help of technology. What are we building We are building a full-stack AI Agentic Platform that powers customer, sales and internal workflows end-to-end. Core capabilities are including but not limited to: *Multi-agent, multi-modal customer interactions. *Agents which can handle agent decision hubs. *Evaluation engines to score and diagnose interactions. *Generating insights from chat to improve SOP, Tools and Product *Flow Fine-tuning & deployment pipelines *Observability with agent telemetry systems Safety, governance, and cost optimization layer. What are we looking for We are looking for builders who have a passion for building great products and have demonstrated this through their projects in recent years. Competencies * Create explainable workflows for multi-agent systems * Design and operate evaluation systems for millions of conversations * Fine-tuning of language models and its deployment workflows * Architect low-latency pipelines for text/voice interactions * Ensure safety, auditability, and compliance by design * Partner with stakeholders including PM, Engineering, Design, Data Scientists, Ops, and Analysts to ship features across business lines * Create standards for prompt design, prompt improvement, agent traits, and safety checks * Build APIs, SDKs, and runtimes for agent execution * Mentor juniors and contribute to platform-wide thought leadership * Experience with multi-agent frameworks (AutoGen, LangGraph, Haystack, ReAct, Supervisor-Executor patterns) * Background in LLM ops including training, data pipelines, GPU scheduling * Experience with Voice & telephony integrations * Knowledge of safety model design o

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day. We are looking for a Senior Product Manager to join our Applied AI organization at Smartsheet. You'll own the roadmap for specialized AI agents and evaluation infrastructure, ship complex agent capabilities end-to-end, and define the standards that enable teams across the company to build agents successfully. You'll report to our Director of Product Management for Applied AI located in our Bellevue, WA office, or you may work remotely from anywhere in the US where Smartsheet is a registered employer. You Will: Own the strategy, roadmap, and execution for specialized AI agents and evaluation infrastructure Ship large, technically complex agent capabilities end-to-end with with a high degree of autonomy Define agentic development standards that enable teams beyond your own to build high-quality agents Mentor and develop junior PMs on your team Build strong cross-functional partnerships with engineering, design, and data science to drive alignment and move work forward Support other duties as assigned You Have: 8-10+ years of product management experience with technically complex platforms Fluency in AI/ML concepts -model evaluation, agent architectures, RAG, prompt engineering, quality measurement Track record of shipping enterprise software from inception to launch Experience building or managing evaluation and quality infrastructure for AI or ML systems (strongly preferred) Comfort operating in high-ambiguity environments with significant autonomy Exceptional communication skills and the ability to influence without auth