**Role Overview** We are seeking a Forward\-Deployed AI Engineer to act as a client\-facing, hands\-on consulting engineer driving end\-to\-end AI/LLM\-enabled solutions. In this role, you will work directly with customers \-from initial discovery through prototyping to production deployment\- to design and build AI\-powered workflows that deliver measurable business impact. You will collaborate with client and internal teams to integrate advanced GenAI techniques into real\-world systems, taking high ownership of outcomes across multiple stakeholders. The role combines hands\-on engineering with consultative engagement, high autonomy, and accountability for production outcomes. **Key Responsibilities** **Consult \& Plan (Discovery \& Solutioning)** * Lead discovery workshops to analyze client workflows and identify opportunities for AI integration * Define AI roadmaps and MVP use cases, including ROI hypotheses, success metrics, and guardrails for responsible AI usage * Align early on data availability, security, and compliance requirements * Document assumptions, trade\-offs, and delivery architectures in client\-ready formats to support clear execution Shape solution options, technical estimates, and delivery dependencies in collaboration with Solution * **Engineering and Sales** **Build \& Deliver (AI/LLM Workflow Engineering)** * Design and implement LLM\-enabled workflows (e.g. RAG pipelines, agent orchestration, vector database integrations, tool use) * Develop integration layers between LLMs and client systems/APIs using modern languages and frameworks (e.g. Python, Node.js) * Build production\-grade systems with reliability patterns such as logging, fallback mechanisms, and basic evaluation hooks * Optimize performance, latency, and cost while adhering to security, privacy, and compliance standards * Coordinate with client platform and data teams to support infrastructure or integration needs Create runbooks and operational documentation for smooth handover and self\-sufficiency * **Enable \& Support (Client Empowerment)** * Deliver training to end\-users, SMEs, and technical teams on solution functionality, prompts, and governance * Provide role\-based runbooks, prompt/playbook templates, and quick\-start guides for client operations * Establish feedback loops and usage dashboards to monitor adoption, quality, latency, and cost Lead post\-deployment iterations to optimize AI workflow performance and user satisfaction * **Sales Support \& Business Development** * Support pre\-sales engagements with discovery input, technical solution outlines, and rapid demo prototypes * Present AI transformation narratives to client stakeholders, translating engineering concepts into business value **Leadership \& Internal Contribution** * Mentor Gorilla Logic and client engineers in applied AI delivery techniques * Contribute learnings and reusable assets back into Construct™ accelerators and internal enablement materials Participate in design reviews, estimation sessions, and capability\-building initiatives * **Required Skills and Experience** * 5–8\+ years in software or product engineering roles with direct experience deploying AI/LLM\-powered solutions * Proven expertise in integrating GenAI tools such as OpenAI, Anthropic, Hugging Face, LangChain, and vector databases * Proficiency in one or more modern programming languages (e.g. Python, TypeScript) and cloud environments (AWS, Azure, GCP) * Experience building production systems that include APIs, event\-driven flows, and containerized deployments (Docker/Kubernetes) * Strong consulting skills with experience in client\-facing workshops, stakeholder engagement, and executive communication Track record of driving measurable AI adoption and workflow improvements in real\-world environments * **Nice to Have** * Experience with AI evaluation harnesses, prompt testing, and guardrails Familiarity with AI observability, LLMOps, and responsible AI design principles * **Out of Scope** * Custom ML model research or training * Ownership of heavy MLOps (feature stores, training pipelines, model lifecycle management)