Jobs at Robots & Pencils

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.

Leads end-to-end data science and machine learning engagements, from framing ambiguous business problems through production deployment. Builds predictive, prescriptive, deep learning, statistical, NLP, forecasting, optimization, and GenAI solutions; designs experiments and applies causal inference. Partners with engineering, product, and business teams, communicates findings to technical and executive audiences, establishes scalable ML processes, mentors junior scientists, and develops reusable tools and architectures.

Leads end-to-end data science and machine learning engagements, from ambiguous problem framing and statistical modeling through production deployment. Builds predictive, prescriptive, deep learning, GenAI, forecasting, NLP, and optimization solutions; designs experiments and causal analyses; and establishes scalable ML processes. Partners with engineers, product managers, and clients, communicates findings to executives, mentors data scientists, researches new approaches, and develops reusable tools and architectures.

Provide high-volume sourcing and recruiting coordination for global technical and delivery roles: proactively source candidates, conduct outreach, manage pipelines and metrics, schedule interviews across time zones, coordinate calendars, maintain tracking and documentation, and support offer logistics to ensure a smooth candidate experience.

Owns enterprise data architecture across warehouses, lakes, lakehouses, streaming systems, governance, modeling, and AI/ML data foundations. Sets platform strategy, architects semantic and unstructured data pipelines, establishes security and observability standards, and drives performance and reliability. Partners with clients and leadership, supports pre-sales, mentors engineers, develops reusable architecture patterns, and remains hands-on with prototypes, production code, and design reviews.

Lead AI and digital strategy engagements from discovery and problem framing through prioritization, roadmaps, business cases, and pilot planning. Map workflows, identify automation opportunities, facilitate executive workshops, prototype AI solutions, and define responsible AI governance. Translate between business and technical teams, align senior stakeholders, support client decision-making, and mentor junior analysts while using modern AI tools and agentic frameworks.

Designs and leads enterprise data platforms, including scalable lakes, warehouses, batch and real-time pipelines, governance, quality, monitoring, and modernization. Builds data infrastructure for production AI/ML systems, including embeddings, vector stores, RAG preparation, feature stores, and training/inference flows. Partners with leadership and cross-functional teams on strategy, makes architectural decisions, establishes engineering standards, and mentors engineers.

Leads architecture and technical strategy for complex cloud, data, and AI/ML systems. Designs scalable multi-cloud platforms, data pipelines, MLOps infrastructure, LLM applications, and production AI systems while establishing DevOps, security, governance, and reliability standards. Partners with clients and leadership, mentors senior engineers, documents architecture, evaluates emerging technologies, and owns high-impact technical decisions. The role also leads cloud migrations and modernization initiatives.

Leads the technical vision and delivery of an enterprise agentic AI program. Oversees architecture, evaluation, deployment, MLOps, reliability, security, and scalability across multiple workstreams. Partners with executives and client stakeholders on roadmaps and high-stakes decisions, aligns data science and engineering teams, establishes technical standards, and mentors senior technical leaders. Requires deep experience in AI/ML, LLMs, agentic systems, AWS, distributed systems, evaluation, and production-scale software engineering.