8+ years in full-stack engineering, solution architecture, or technical consulting with client-facing experience.
Strong Python programming skills; familiarity with TypeScript, Java, or Go.
Hands-on experience with Large Language Models (LLMs) and agent orchestration.
Knowledge of Retrieval-Augmented Generation (RAG) and vector databases.
Experience designing APIs and data pipelines, and implementing software architectures.
Cloud-native engineering experience with AWS, Azure, or GCP.
Ability to communicate technical decisions and business value effectively.
Responsibilities
Engage with clients to understand business challenges and develop AI solutions.
Design and deploy AI applications from proofs of value to production.
Develop agentic AI workflows using LLMs and orchestration techniques.
Build and evaluate RAG solutions with embeddings and vector databases.
Implement APIs, data pipelines, and enterprise integrations.
Create cloud-native solutions using AWS, Azure, or GCP with CI/CD.
Assess AI solutions for performance, reliability, and security.
Benefits
Opportunity to work on cutting-edge AI technologies.
Collaborative environment with senior stakeholders.
Hands-on involvement in transforming business challenges into AI solutions.
Potential for career growth in a rapidly evolving field.
Full Job Description
Job Title: Forward Deployed Engineer - Agentic AI
Salary: $155,000 - $220,000
Role Summary
We are seeking an experienced Forward Deployed Engineer - Agentic AI to combine senior client engagement with hands-on engineering to transform business challenges into practical AI solutions. This role involves developing rapid proofs of value and advancing successful solutions toward minimum viable products (MVPs) and production deployment.
The ideal candidate will have a strong engineering or solution architecture background, practical experience building LLM-powered applications and agentic workflows, and the ability to deliver secure, reliable, and scalable AI solutions that demonstrate measurable business value. Key Responsibilities
Engage with clients and senior stakeholders to understand business challenges and translate them into practical AI solutions.
Design, build, and deploy AI-powered applications, taking solutions from rapid proofs of value toward MVP and production delivery.
Develop agentic AI workflows using LLMs, agent orchestration, tool calling, planning and execution loops, and memory patterns.
Build and evaluate Retrieval-Augmented Generation (RAG) solutions using embeddings, chunking strategies, vector databases, and retrieval evaluation methods.
Design and implement APIs, data pipelines, enterprise integrations, and sound software architectures.
Develop cloud-native solutions using AWS, Azure, or GCP, incorporating containers, CI/CD pipelines, and observability.
Personally implement AI solutions, develop evaluation methods, and assess their performance and reliability.
Address security, access controls, human oversight, reliability, latency, and cost throughout solution development and deployment.
Explain technical decisions, solution trade-offs, and measurable business value to senior business and technology stakeholders.
Support the transition of successful AI prototypes into reliable, production-ready enterprise solutions.
Required Qualifications
8+ years of experience in full-stack engineering, solution architecture, or technical consulting, with significant client-facing delivery experience.
Strong hands-on Python programming skills, plus experience with TypeScript, Java, or Go.
Practical experience with Large Language Models (LLMs), agent orchestration, tool calling, planning and execution loops, and memory patterns.
Experience with RAG, embeddings, chunking, vector databases, and retrieval evaluation.
Experience designing and implementing APIs, data pipelines, enterprise integrations, and sound software architectures.
Cloud-native engineering experience with AWS, Azure, or Google Cloud Platform (GCP).
Experience with containers, CI/CD, and observability.
Ability to communicate technical decisions and demonstrate measurable business value to senior stakeholders.
Demonstrated hands-on engineering ability, including personally building AI solutions and developing evaluation methods.
Preferred Qualifications
Production experience delivering Generative AI (GenAI) solutions.
Experience integrating enterprise platforms such as ServiceNow, Salesforce, SAP, Microsoft technologies, Databricks, or Snowflake.
Experience addressing AI security, access controls, human oversight, reliability, latency, and cost in production environments.
Ideal Candidate Profile
The ideal candidate is a hands-on engineer who combines technical depth with strong client-facing delivery skills. They can translate business needs into working agentic AI solutions, evaluate their effectiveness, and guide implementations from proof of value through production while balancing security, reliability, performance, and cost.
