Manager, Forward Deployed Engineering

Accountabilities: Lead and develop a distributed engineering team: Hire, mentor, and grow forward deployed engineers across the United States and Latin America, fostering a culture of trust, accountability, collaboration, and consistent engineering standards across time zones. Oversee client delivery: Take ownership of scope, quality, timelines, and business outcomes across multiple concurrent engagements, ensuring that AI solutions move from initial discovery to reliable production deployment. Drive technical client engagement: Lead demonstrations, architecture discussions, and delivery workshops, helping clients understand how emerging AI development tools and agentic technologies can address their specific business challenges. Remain hands-on technically: Contribute to prototypes, review technical designs and code, and help engineers overcome challenges involving agentic workflows, tool integrations, Model Context Protocol (MCP), and AI evaluation. Identify growth opportunities: Recognize opportunities to extend existing engagements, collaborate with account leadership, and help define the scope and direction of subsequent project phases. Develop reusable engineering practices: Establish technical patterns, accelerators, documentation, and playbooks that improve delivery consistency, efficiency, and quality across the distributed team. Coordinate cross-functional execution: Partner with data, cloud, and digital engineering specialists to deliver integrated solutions that address broader client needs. Promote responsible AI implementation: Incorporate appropriate governance, security, evaluation, and responsible-use practices into enterprise AI solutions. Requirements Professional experience: At least 7 years of software engineering experience, including 2 or more years leading engineers or technical teams, ideally in distributed environments. Client-facing delivery: Demonstrated experience delivering technical solutions in consulting, solutions engineering, professional services, or forward deployed engineering environments. AI and LLM expertise: Hands-on experience building applications with large language models and agentic architectures, including tool use, retrieval-augmented generation (RAG), workflow orchestration, and evaluation. Software engineering skills: Strong proficiency in Python or TypeScript, with solid knowledge of API design, continuous integration and continuous delivery (CI/CD), and cloud platforms such as Azure, AWS, or Google Cloud Platform. Production delivery: A proven ability to navigate ambiguous business problems, make sound technical decisions, and deliver reliable systems into production. Communication and collaboration: Excellent written and spoken English, with the ability to explain complex technical concepts clearly to both executive stakeholders and engineering teams. Time zone alignment: Availability for meaningful working-hours overlap with the US Eastern and Central time zones. Preferred technical experience: Familiarity with Claude Code, Claude Cowork, the Anthropic API, or the Model Context Protocol (MCP). Enterprise AI knowledge: Experience with AI governance, security, and responsible AI practices in enterprise environments is an advantage. Additional qualifications: Fluency in Spanish or Portuguese, a background in data science or formal engineering education, and experience in consulting or professional services are desirable. Leadership qualities: Strong ownership of outcomes, intellectual curiosity, the confidence to challenge assumptions constructively, and a commitment to coaching and developing others. Distributed team management: Ability to communicate effectively through clear documentation, asynchronous collaboration, and well-defined decisions while adapting to rapidly evolving technologies. Benefits Remote work flexibility: Remote opportunities for candidates based in the United States, with a distributed team operating across the Americas. Technical innovation: Opportunities to work with advanced AI technologies, agentic systems, and modern engineering tools. Leadership and career development: The opportunity to build and mentor a distributed engineering team while helping shape an expanding applied AI practice. Meaningful client impact: Direct involvement in solving complex business challenges and bringing AI solutions into production. Cross-functional collaboration: Exposure to multidisciplinary projects spanning AI, data, cloud, and digital engineering. Professional growth: Opportunities to develop technical expertise, strengthen client advisory skills, and influence engineering standards and delivery practices. Compensation: Salary details and the availability of additional benefits have not been specified in the provided job description.