Forward Deployment Engineer

About the Role

We are developing a top-tier internal AI engineering team focused on creating and scaling advanced enterprise AI solutions tailored for complex operational settings. Our approach centers on building proprietary AI products that evolve continuously, rather than adopting pre-existing commercial solutions.

Role Overview

As a Forward Deployment AI Engineer, you will steer the technical direction of an AI team embedded within the business, collaborating closely with stakeholders to grasp business challenges and root causes. You will design, develop, deploy, and maintain autonomous AI systems that generate tangible business value.

Key Responsibilities

• Begin each project by thoroughly understanding business problems, goals, and expected results, working hand-in-hand with stakeholders to lead solutions from initial discovery through to production deployment.
• Design, implement, and enhance enterprise-grade AI applications utilizing agentic development methodologies as your primary engineering practice.
• Create AI agents capable of multi-step reasoning, managing tools and APIs, context retention, exception handling, and human-in-the-loop workflows at scale.
• Develop Retrieval-Augmented Generation (RAG) systems, including data ingestion processes, chunking, embeddings, vector search optimization, grounding, and source traceability.
• Integrate AI systems with enterprise platforms via Model Context Protocol (MCP), REST APIs, OpenAPI specs, webhooks, and event-driven architectures.
• Build reusable, self-service AI services and interfaces deployable across varied business domains.
• Apply rigorous LLM engineering best practices such as tool invocation, schema validation, retries, fallback mechanisms, and guardrails.
• Ensure solution quality throughout its lifecycle with robust testing, evaluation, logging, version control, observability, and continuous feedback loops.
• Optimize deployed AI solutions for reliability, accuracy, security, latency, performance, and cost-effectiveness.
• Ensure alignment with security policies, privacy standards, access controls, audit requirements, and responsible AI governance.
• Own business outcomes in your domain, guaranteeing delivery success and observable impact.
• Create reusable AI frameworks, standards, and engineering patterns to enhance internal AI capabilities.
• Collaborate productively with Product Owners, AI Architects, and engineering teams, critically assessing when AI solutions are appropriate.
• Define technical strategies, architectural standards, and make informed build-versus-buy decisions for AI projects.
• Design multi-agent systems featuring agent communication patterns and evaluation frameworks to ensure scalability and robustness.
• Manage relationships with external AI technology vendors while fostering internal skills development.
• Mentor team members, conduct technical evaluations, and drive improvements in quality, security, and cost efficiency.

Required Qualifications and Experience

• A strong curiosity and passion for deciphering intricate business and operational problems.
• A business-oriented, human-centered mindset that emphasizes AI augmenting people rather than replacing them.
• Proficient English communication skills and experience working within multicultural international teams.
• Minimum 8 years of experience developing production-ready software, including at least 4 years with Generative AI, Large Language Models, or applied machine learning.
• At least 1 year of direct experience designing and deploying agentic AI systems.
• Proven track record delivering AI solutions at scale and setting technical direction.
• Hands-on experience or deep familiarity with Model Context Protocol (MCP) to connect AI agents with tools, APIs, data, and systems.
• Strong programming proficiency in Python plus expertise in one or more of TypeScript/JavaScript, Java, or C#.
• Familiarity with modern engineering methods including asynchronous programming, FastAPI, Pydantic, continuous integration/deployment (CI/CD), testing protocols, source control, logging, and error management.
• Experience using at least one agent framework or enterprise AI platform such as LangGraph, Semantic Kernel, CrewAI, AutoGen, OpenAI Agents SDK, Microsoft Foundry, Amazon Bedrock AgentCore, or Google Vertex AI/Gemini.
• Experience with vector databases and search platforms including Azure AI Search, pgvector, Pinecone, Weaviate, or OpenSearch.
• Experience integrating enterprise systems using APIs, managed identities, middleware, webhooks, messaging queues, and cloud-native architectures.
• Proven ability deploying solutions in cloud environments with containerization, monitoring, and observability tooling.
• Sound judgement balancing trade-offs involving quality, latency, reliability, security, governance, and cost.

Preferred Experience

• Prior experience in industries such as aviation, transportation, logistics, supply chain, cargo operations, or customer service in complex operational contexts.
• Background in traditional machine learning, data science, or advanced analytics.
• Experience across the full software stack including front-end, APIs, and back-end development.
• Designing enterprise-scale AI agents for operational workflows.
• Working with voice AI, email automation, CRM integration, workflow automation, or multilingual AI implementations.
• Building evaluation frameworks and AI quality measurement systems, including golden datasets, regression testing, simulation-based validations.
• Designing multi-agent architectures, agent registries, and tool orchestration standards.
• Leading vendor engagements while cultivating internal AI team capabilities.

Education

• Master’s degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, Machine Learning, or related technical discipline, or equivalent hands-on experience.
• Certifications relevant to Cloud AI, Generative AI, Agentic AI, MLOps, or similar fields are beneficial.