Forward Deployed AI Software Engineer

Locus Robotics•Wilmington, MA

8h•$150,000 - $200,000

About The Position

Locus Robotics is hiring a Forward Deployed AI Engineer to partner with teams across the organization to identify opportunities to eliminate manual work, simplify complex processes, and improve employee productivity with AI. The role involves translating business challenges into AI agents, intelligent applications, and automated workflows, then rapidly building and deploying solutions connected to enterprise systems. Successful solutions may be developed into reusable approaches for teams across the company. This is a hands-on enterprise AI development role focused on internal business transformation, rather than Locus product development or AI model research. The engineer will work directly with users and stakeholders, taking ideas from concept to production while ensuring solutions are secure, reliable, measurable, and appropriate for responsible enterprise use.

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, including experience building APIs, integrations, and automation that connect applications with enterprise systems and data.
Hands-on expertise with large language models, commercial or open model APIs, and development of AI agents and agentic workflows.
Ability to build and deploy enterprise AI agents or agentic workflows using platforms such as Claude and Glean.
Ability to partner directly with business stakeholders, translate ambiguous needs into practical technical solutions, and iterate 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.
Experience 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.

Responsibilities

Work with teams across Locus to identify high-impact opportunities for AI to improve manual and complex workflows.
Translate loosely defined business challenges into practical AI solutions, moving 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, or other appropriate methods.
Build retrieval and grounding capabilities that enable secure use of structured and unstructured enterprise data, using techniques such as ingestion, metadata, embeddings, search, and reranking.
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 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 emerging AI models, agent frameworks, integration standards, and enterprise AI practices; share knowledge and help teams adopt approved AI capabilities.