Forward Deployed AI Engineer

Locus Robotics•Wilmington, MA

5d•$150,000 - $200,000•Hybrid

About The Position

Locus Robotics is hiring a Forward Deployed AI Engineer to partner with teams across the organization and identify opportunities to reduce manual work, simplify complex processes, and improve employee productivity through AI. The engineer will turn real business challenges into AI agents, intelligent applications, and automated workflows, working directly with internal stakeholders to understand current processes and rapidly develop and deploy solutions connected to enterprise systems. Successful solutions may be developed into reusable approaches for use across the company. This is a hands-on enterprise AI development role focused on internal business transformation, not Locus product development or AI model research. The role takes solutions from concept through production and helps ensure they are secure, reliable, measurable, and suitable for responsible enterprise use. It is based at Locus Robotics Headquarters in Wilmington, Massachusetts, with a three-day onsite requirement. Relocation is not available, and only local candidates are being considered. Candidates must be eligible to work in the United States or have active H-1B status requiring a change-of-employer transfer; the company does not sponsor new initial H-1B lottery filings.

Requirements

Bachelor's degree in computer science, engineering, or a related technical discipline.
At least 4 years of professional software engineering experience building, deploying, and supporting production applications.
Strong proficiency in Python, with experience building APIs, integrations, and automation connecting applications with enterprise systems and data.
Hands-on expertise with large language models, commercial or open model APIs, and AI agents and agentic workflows.
Ability to build and deploy enterprise AI agents or agentic workflows using platforms such as Glean, Claude, or comparable enterprise AI, agent, and orchestration platforms.
Ability to work directly with business stakeholders, translate ambiguous needs into practical technical solutions, and iterate quickly based on user feedback.
Working knowledge of retrieval-augmented generation (RAG), including embeddings, semantic or hybrid search, prompt and context engineering, and structured model outputs.
Demonstrated success integrating enterprise platforms such as Microsoft 365, Salesforce, Slack, Jira, Confluence, ERP systems, and modern data platforms.
Strong understanding of enterprise application security, including authentication, authorization, OAuth/OIDC, role-based access controls, and permission-aware data access.
Familiarity with MCP or similar standards for securely connecting AI models and agents with enterprise tools, systems, and data.
Knowledge of vector databases, enterprise search, and knowledge retrieval systems.
Proficiency with modern software engineering practices, including automated testing, version control, CI/CD, application observability, and production support.
Demonstrated ownership and sound judgment, with the ability to work effectively across teams and organizational boundaries.
Excellent written and verbal English communication skills, including the ability to explain technical concepts and tradeoffs to technical and non-technical audiences.
Must be eligible to work in the United States or have active H-1B status requiring a change-of-employer transfer; new initial H-1B lottery filings are not sponsored.
Must be a local candidate able to meet the three-day onsite requirement in Wilmington, Massachusetts; relocation is not available.

Responsibilities

Work with teams across Locus to understand existing workflows, identify high-value AI opportunities, and improve manual or complex processes.
Translate loosely defined business challenges into practical AI solutions, taking them from concept and prototype to secure, production-ready tools and workflows.
Design, build, and deploy AI agents, assistants, and automated workflows using large language models, retrieval, tool integrations, structured outputs, and appropriate human oversight.
Build secure, reliable integrations between AI solutions and enterprise systems and data using APIs, connectors, MCP, and other suitable methods.
Build retrieval and grounding capabilities that allow AI solutions to securely use structured and unstructured enterprise data, including ingestion, metadata, embeddings, search, and reranking as appropriate.
In partnership with IT, Security, and governance teams, incorporate authentication, authorization, audit logging, observability, cost controls, and human-in-the-loop safeguards.
Build automated evaluations and regression tests, support deployment and monitoring, and continuously improve performance, reliability, cost, and user outcomes.
Measure solution impact through business outcomes such as reduced cycle time, hours saved, improved quality, increased capacity, and better customer outcomes.
Turn successful implementations into reusable components, templates, reference architectures, and documented patterns.
Stay current with AI models, agent frameworks, integration standards, and enterprise AI practices; share knowledge and help teams adopt approved AI capabilities.