Jobs at Robots & Pencils

Leads end-to-end delivery of complex generative and agentic AI products, from discovery and prototyping through production rollout. Owns roadmaps, agent behavior, prompts, tool schemas, evaluations, metrics, guardrails, backlog prioritization, and sprint execution. Partners with engineering and senior client stakeholders to balance business value, technical feasibility, cost, latency, quality, and safety. Requires hands-on AI prototyping, AWS experience, technical fluency, client communication, and mentoring capability.

Leads end-to-end design and delivery of production AI/ML systems, including architecture, model development, scalable platforms, RAG pipelines, agentic systems, evaluation, observability, security, and cost optimization. Partners with product and leadership, establishes engineering standards, mentors engineers, and owns complex technical initiatives through production while ensuring reliability, scalability, safety, and business impact.

Leads the design, implementation, and operation of cloud infrastructure, CI/CD pipelines, Kubernetes platforms, infrastructure as code, observability, cost management, and DevSecOps practices. The role also supports AI/ML platform infrastructure, including model serving, GPU orchestration, LLM gateways, vector stores, and related deployment pipelines. Responsibilities include incident response, technical collaboration, platform strategy, standards development, and mentoring junior engineers.

Leads end-to-end data science and machine learning engagements, from ambiguous problem framing and model development through production deployment. Builds predictive, prescriptive, deep learning, and GenAI solutions; conducts experimentation and causal inference; analyzes complex data; and communicates findings to technical and executive audiences. Provides technical leadership, mentors data scientists, partners with engineering and product teams, and develops reusable tools and scalable ML processes.

Design, build, and operate scalable data pipelines, warehouses, lakes, and integrations supporting analytics, AI, and product systems. Develop data models, validation, monitoring, and production AI/ML data flows involving embeddings, vector stores, RAG, feature stores, and training or inference data. Collaborate across analytics, AI, and product teams, contribute to architecture and governance decisions, review code, and own data systems end to end.

Lead architecture and delivery of complex full-stack platforms, including scalable front-end systems, backend APIs, databases, integrations, AI services, and production reliability practices. Establish engineering standards, design systems, testing, security, observability, and CI/CD practices. Mentor engineers, lead technical discussions and code reviews, make architectural decisions, and communicate tradeoffs to technical and business stakeholders.

Leads end-to-end design, development, and deployment of scalable AI/ML systems. Defines architecture and engineering standards, builds ML platforms and RAG pipelines, optimizes models and costs, and ensures reliability, security, observability, and responsible AI practices. Partners with product and leadership, communicates technical tradeoffs, leads design reviews, mentors engineers, and delivers production agentic systems using cloud, containerization, and AI-forward development tools.

Design, build, deploy, and maintain production AI/ML systems, including models, data pipelines, training workflows, agentic systems, and RAG pipelines. Monitor and optimize reliability, scalability, performance, and costs while contributing to architecture decisions. Collaborate with product, engineering, and data teams, communicate technical tradeoffs, support junior engineers, and apply responsible AI, security, observability, and containerization practices.

Lead large-scale AI, digital, and cloud transformation programs from strategy through launch and realized business value. Manage multiple Agile teams, workstreams, dependencies, governance, KPIs, budgets, risks, and executive stakeholder alignment. Translate business needs into technical requirements, facilitate architecture discussions, establish AI lifecycle and evaluation standards, and guide decisions involving model risk, observability, and agentic solutions. Mentor delivery managers, support staffing and hiring decisions, and contribute to client expansion opportunities and delivery practice improvements.

Lead the architecture, development, optimization, and production deployment of scalable AI and machine learning systems. Build ML platforms, pipelines, agentic systems, RAG solutions, and LLM evaluation and observability capabilities. Establish engineering standards for reliability, security, responsible AI, cost optimization, and scalability. Partner with product and leadership, communicate technical tradeoffs, lead design reviews, mentor engineers, and own ambiguous, high-impact initiatives through production delivery.

Leads the architecture, development, optimization, and production deployment of complex AI and machine learning systems. Builds scalable ML platforms, pipelines, and infrastructure; establishes engineering standards; guides responsible AI practices; and mentors engineers. Partners with product and leadership teams to shape AI strategy, communicates technical tradeoffs, and drives high-stakes initiatives focused on reliability, scalability, security, cost, and safety. The role requires deep generative AI, agentic systems, RAG, cloud, and production engineering expertise.

Lead the architecture, development, deployment, and adoption of production AI/ML systems within client environments. Build scalable ML platforms, agentic systems, RAG pipelines, and integrations; resolve complex technical challenges; establish responsible AI and engineering standards; communicate tradeoffs to stakeholders; support client enablement; and mentor engineers. The role requires deep expertise in generative AI, AWS, cloud infrastructure, observability, security, cost optimization, Docker, and Kubernetes.

Lead the architecture, development, deployment, and adoption of production AI/ML systems within client environments. Build scalable ML platforms, agentic systems, RAG pipelines, and integrations; resolve complex enterprise deployment challenges; and establish standards for reliability, security, cost, and responsible AI. Partner with technical and business stakeholders, support client enablement, lead design reviews, and mentor engineers while delivering solutions from research through production.

Leads the architecture, development, optimization, and production delivery of complex AI/ML and agentic systems. Builds scalable ML platforms, pipelines, and infrastructure; establishes engineering standards; drives AI strategy; and mentors engineers. The role requires expertise in generative AI, AWS, RAG, evaluation and observability, AI safety, API integration, cost optimization, Docker, Kubernetes, and AI coding tools.

Design, build, deploy, and maintain production AI/ML systems, including models, data pipelines, training workflows, RAG pipelines, agentic systems, and APIs. Monitor and improve reliability, scalability, performance, and cost. Collaborate with product, engineering, and data teams, contribute to architecture decisions, communicate technical tradeoffs, participate in code reviews, and support junior engineers. Apply responsible AI, safety, observability, and security practices in cloud-native environments.

Defines AI architecture and technical strategy while leading production implementation of scalable ML platforms, LLM applications, agentic systems, RAG architectures, APIs, and cloud-native infrastructure. Establishes standards for reliability, security, observability, governance, and cost optimization. Partners with leadership on the AI roadmap, makes high-impact architectural decisions, mentors engineers, and drives technical direction across teams and client engagements.

Leads architecture and delivery of production-grade AI, multi-agent, cloud, data, and ML systems. Defines AWS-centered technical strategy, builds agentic platforms, designs data pipelines and MLOps infrastructure, and establishes DevOps, security, governance, observability, and cost standards. Serves as the senior technical authority for clients and cross-functional teams, mentors engineers, evaluates emerging technologies, and drives complex solutions from research through production.

Develop and maintain automated UI, API, integration, and AI/ML tests. Validate model outputs, data pipelines, LLM behavior, RAG quality, safety, and model drift. Integrate testing into CI/CD, track defects, analyze results, report quality status, and collaborate with developers, ML engineers, and data scientists. The role also contributes to QA strategy, process improvements, test coverage, and end-to-end ownership of testing responsibilities.

Leads architecture and technical strategy for complex cloud, data, and AI/ML systems. Designs scalable multi-cloud platforms, data pipelines, MLOps and LLM applications; establishes infrastructure, DevOps, security, governance, and observability standards. Partners with clients and leadership, leads modernization initiatives, resolves difficult integration challenges, mentors senior engineers, and evaluates emerging technologies.

Leads product strategy, vision, roadmaps, discovery, prioritization, and delivery for complex client engagements. Partners with executive stakeholders, defines success metrics, drives cross-functional alignment, facilitates strategic workshops, mentors product managers, and resolves complex product and technical challenges. The role also applies AI tools to product discovery, research synthesis, documentation, and prototyping while guiding the delivery of AI-powered products.