Agentic AI Forward Deployed Engineer

BICP, a San Diego-based consulting firm specializing in AI, Data & Analytics, is seeking an experienced Agentic AI Forward Deployed Engineer to join our team in an embedded capacity with one of our strategic clients in the apparel and retail industry. This role is embedded directly with business, product, data science and technology stakeholders. You will translate high-value business problems into working agentic products, moving rapidly from discovery and prototype through production deployment and adoption.

You will partner closely with an Agentic AI Product Manager and Principal AI Platform Architect while helping experienced internal engineers and data scientists develop practical agentic AI capabilities. This is not a research-only, advisory or traditional machine-learning role. Success requires someone who can work through ambiguity, write production-quality code, integrate agents with enterprise systems and remain accountable for whether the resulting product delivers measurable business value.

Build Agentic AI Products Design, develop and deploy production-grade AI agents that reason across multistep workflows, access enterprise data, invoke tools and APIs, and take actions within defined operating boundaries. Translate business requirements into working AI products through rapid discovery, prototyping, testing and iterative delivery. Create reusable components, services and implementation patterns that accelerate subsequent AI products. Take direct ownership of implementation—not simply technical design or vendor management.

Connect AI to Enterprise Data and Systems Integrate agents with APIs, databases, SaaS platforms and internal business applications. Build secure retrieval and knowledge capabilities across structured and unstructured enterprise data. Develop and maintain RAG pipelines, embeddings, vector and hybrid search, metadata filters and grounding mechanisms. Integrate AI products with cloud data platforms such as Snowflake, Databricks and Azure-based services. Build evaluation into the development lifecycle using representative test cases, golden datasets and automated regression testing. Measure task completion, answer quality, groundedness, tool-selection accuracy, latency, cost and escalation rates. Implement end-to-end logging, tracing, observability and production monitoring. Optimize model selection, prompts, context usage and architecture for quality, performance and cost.

Apply least-privilege access controls to agents, tools and enterprise data. Protect against prompt injection, unintended tool execution, sensitive-data exposure and data exfiltration. Ensure solutions meet enterprise requirements for privacy, compliance and responsible AI.

Challenge requests when agentic AI is not the appropriate solution. Help define measurable product outcomes rather than treating a functioning prototype as success. Pair with internal engineers, data scientists and technical product team members who are developing agentic AI experience. Teach practical patterns for agent design, retrieval, evaluation, security and production deployment. Document reusable components, engineering standards and lessons learned. 7+ years of professional software, data, machine-learning or AI engineering experience.

Significant hands-on experience building applications in Python and integrating RESTful APIs and enterprise systems.

Demonstrated experience taking generative AI applications from prototype into production.

Direct experience building at least one production agentic system involving multistep execution, tool use or autonomous workflow orchestration.

Strong understanding of LLMs, prompting, structured outputs, tool calling, RAG, embeddings and vector search.

Experience establishing evaluation, observability and monitoring for nondeterministic AI systems.

Experience deploying applications within modern cloud environments.

Experience mentoring engineers or data scientists in an unfamiliar or rapidly evolving technology.

Experience with Azure OpenAI, Azure AI services or comparable managed AI platforms. Experience integrating with Snowflake, Databricks or similar enterprise data environments. Experience with low-code or commercial agentic AI platforms and the ability to integrate them without creating unnecessary vendor dependence. Familiarity with model gateways, prompt and model versioning, LLMOps and AI governance. Background in retail, apparel, consumer products, merchandising, planning, supply chain, marketing or customer experience. Experience operating in a consulting, customer-facing or forward-deployed engineering environment.

What Strong Performance Looks Like

Within the first several months, the Forward-Deployed Agentic AI Engineer will: Develop a clear understanding of the company's business priorities, data environment and technology ecosystem. Help determine which capabilities should come from existing platforms and which should be built internally. Increase the confidence and practical agentic AI capability of the internal engineering and data science teams.

The ideal candidate combines the instincts of a product-minded engineer, the technical depth of an applied AI practitioner and the adaptability of a consultant working inside the client environment. You move quickly without confusing speed with shortcuts, and you understand that the goal is not to build the most sophisticated agent - it is to deliver the simplest dependable solution that creates meaningful business value.

BICP is a San Diego-based AI, Data & Analytics consultancy that helps organizations close the gap between strategic priorities and the specialized capabilities required to execute them. Our Forward Deployed practitioners work alongside client teams, bringing deep expertise across AI/ML, Data, Analytics, Product, and Architecture to move initiatives from idea to implementation, production, and measurable business impact.