Forward Deployed Engineer

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.
• Deploying SeekrFlow and agent applications intro customer-managed environments, containerizing and orchestrating them across cloud, hybrid, and on-prem deployments (AWS, Azure, GCP, private cloud).
• Packaging, configuring, and releasing SeekrFlow's containerized microservices into customer Kubernetes clusters using Docker and Helm, and managing versioned upgrades and rollbacks.
• Configuring authorization and identity for the platform, including SSO (SAML / OIDC) and service-to-service authentication.
• 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.
• Codifying best practices into reusable playbooks, evaluation frameworks, and deployment accelerators that scale AI adoption across industries.

In This Role We Value

• Capacity to continuously learn, operate independently, and make decisions with minimal guidance.
• Ability to work effectively in teams with both technical and non-technical members, thriving in a fast-paced, ever-changing environment with evolving goals and user collaboration.
• Enthusiasm for tackling technical challenges creatively using data structures, storage systems, cloud infrastructure, front-end frameworks, and other technical tools.
• Passion for leveraging large-scale data to address significant business challenges.

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, Mathematics, Physics, or Data Science (advanced degree a plus).
• 6+ years in software, platform, or infrastructure engineering, with a track record of deploying operating production systems in customer or enterprise envrionments.
• Deep, hands-on experience deploying containerized applications to Kubernetes across multiple cloud providers (AWS, Azure, GCP, OCI, or equivalents) and on-prem, including Docker and Helm for packaging, configuration, and release.
• Proven experience designing, deploying, and maintaining production-grade agentic AI systems that meet enterprise security, compliance, and performance standards
• Experience with monitoring, logging, and observability frameworks for deployed AI/ML systems (Prometheus, OpenTelemetry, Grafana, Logfire)
• Experience with CI/CD pipelines, version control (GitHub/GitLab), and infrastructure as code (Terraform, CloudFormation, etc.).
• Hands-on expertise with AI/ML and LLM frameworks (e.g., PyTorch, Hugging Face, LangChain, vLLM), including model fine-tuning and agent deployment.
• 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 25–50%, depending on client and team needs.

Preferred Qualifications

• Familiar with common graph databases and graph languages (e.g., Neo4j, AGE, SPARQL, Cypher)
• Familiarity with vector databases, retrieval-augmented generation (RAG) architectures, and enterprise data integration patterns.
• 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.
• Familiar with front end frameworks (e.g., React) and capable of building insightful and engaging application front ends
• Strong communication skills — ability to present complex technical concepts clearly to diverse audiences.

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