Forward Deployed Engineer

Seekr•Austin, TX

18d•$170,000 - $225,000•Hybrid

About The Position

Forward Deployed Engineers (FDEs) work alongside our clients, embedding with their teams to tackle their hardest technical and operational problems. You won’t just design solutions — you’ll deploy AI systems in production, applying large language models, fine-tuning, and agentic workflows to unlock real business value. With SeekrFlow, our platform for trustworthy, document-grounded, agentic AI, you’ll transform how organizations leverage their data — building solutions that are explainable, scalable, and production-ready. As an FDE, you’ll be on the front lines of AI adoption, shaping how enterprises bring advanced AI into their most critical missions. Core Responsibilities As an FDE, your work will directly shape our clients’ missions and create real-world impact. Operating in small, dynamic teams, you’ll take full ownership of high-impact projects from start to finish, including: Operationalizing AI PoCs by transforming demos and prototypes into robust, production-grade AI systems. Designing and deploying data pipelines and retrieval frameworks that prepare enterprise data for LLM training, fine-tuning, and RAG-based applications. Fine-tuning, evaluating, and deploying large language models and agentic workflows, ensuring outputs remain grounded, explainable, and compliant. Integrating SeekrFlow AI agents with customer environments, containerizing and orchestrating them across cloud, hybrid, and on-prem deployments (AWS, Azure, GCP, private cloud). Embedding with client engineering teams to debug model behavior, optimize inference performance, and implement monitoring/guardrails for safe operation. Codifying best practices into reusable playbooks, evaluation frameworks, and deployment accelerators that scale AI adoption across industries.

Requirements

Bachelor’s degree in Computer Science, Engineering, Mathematics, Physics, or Data Science (advanced degree a plus).
6+ years of professional experience in software engineering, data engineering, or applied ML, with a track record of building and deploying production systems.
Hands-on expertise with AI/ML and LLM frameworks (e.g., PyTorch, Hugging Face, LangChain, vLLM), including model fine-tuning and agent deployment.
Proven experience designing, deploying, and maintaining production-grade systems that meet enterprise security, compliance, and performance standards.
Strong background in on-premise, private cloud, and multi-cloud environments (AWS, Azure, GCP), including experience with containerization and orchestration (Docker, Kubernetes).
Proficiency in at least one modern programming language (Python, Java, C++, TypeScript/JavaScript, or similar), with the ability to learn and adapt quickly.
Ability to work directly with client stakeholders — technical teams, business leaders, and end users — to translate needs into deployed solutions.
Willingness to travel 25–75%, depending on client and team needs.
Must be a U.S. Citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State or U.S. Department of Commerce.

Nice To Haves

Familiar with common graph databases and graph languages (e.g., Neo4j, AGE, SPARQL, Cypher)
Familiar with front end frameworks (e.g., React) and capable of building insightful and engaging application front ends
Experience with CI/CD pipelines, version control (GitHub/GitLab), and infrastructure as code (Terraform, CloudFormation, etc.).
Familiarity with vector databases, retrieval-augmented generation (RAG) architectures, and enterprise data integration patterns.
Exposure to monitoring, logging, and observability frameworks for deployed AI/ML systems.
Background in responsible AI practices and familiarity with Trustworthy AI principles.
Experience building custom client-facing applications or agentic workflows from prototype to scalable deployment.
Strong communication skills — ability to present complex technical concepts clearly to diverse audiences.

Responsibilities

Operationalizing AI PoCs by transforming demos and prototypes into robust, production-grade AI systems.
Designing and deploying data pipelines and retrieval frameworks that prepare enterprise data for LLM training, fine-tuning, and RAG-based applications.
Fine-tuning, evaluating, and deploying large language models and agentic workflows, ensuring outputs remain grounded, explainable, and compliant.
Integrating SeekrFlow AI agents with customer environments, containerizing and orchestrating them across cloud, hybrid, and on-prem deployments (AWS, Azure, GCP, private cloud).
Embedding with client engineering teams to debug model behavior, optimize inference performance, and implement monitoring/guardrails for safe operation.
Codifying best practices into reusable playbooks, evaluation frameworks, and deployment accelerators that scale AI adoption across industries.