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 Seekr Flow, 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.

• Operationalizing AI PoCs by transforming demos and prototypes into robust, production-grade AI systems.
• Deploying Seekr Flow 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 Seekr Flow'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.

• 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.

• 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 environments.
• 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, Open Telemetry, Grafana, Logfire).
• Experience with CI/CD pipelines, version control (Git Hub/Git Lab), and infrastructure as code (Terraform, Cloud Formation, etc.).
• Hands‑on expertise with AI/ML and LLM frameworks (e.g., PyTorch, Hugging Face, Lang Chain, vLLM), including model fine‑tuning and agent deployment.
• Proficiency in at least one modern programming language (Python, Java, C++, Type Script/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.