Forward Deployed Engineer
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Key skills for this role
About the Role
Ansen Technology is hiring Forward Deployed Engineers to work directly with customers, designing and deploying AI-enabled systems.
Key Skills for This Role
Responsibilities
- Work directly with users and technical stakeholders to understand workflows, data sources, pain points, and success criteria
- Translate ambiguous operational requirements into clear technical designs and deployable solutions
- Build production grade applications, APIs, integrations, data workflows, and AI powered features
- Develop LLM, RAG, agentic, extraction, classification, and decision support workflows
- Deploy and operate solutions using cloud platforms, containers, CI/CD, monitoring, logging, testing, and security controls
- Debug issues across application, data, model, infrastructure, and integration layers
- Scale successful pilots into production rollouts across multiple teams or customer environments
- Identify reusable implementation patterns and convert customer feedback into actionable product requirements
Requirements
- Strong software engineering experience in backend, full stack, data engineering, AI engineering, cloud infrastructure, or enterprise integrations
- Demonstrated ability to build and ship working software in real operational environments
- Proficiency in one or more modern programming languages such as Python, TypeScript, JavaScript, Go, or Java
- Practical experience with APIs, databases, authentication, deployment workflows, and production debugging
- Strong problem solving ability in ambiguous and fast moving environments
- Excellent communication skills and confidence working directly with technical and non technical stakeholders
Full Job Posting
Role Overview
- We are building a team of Forward Deployed Engineers (FDEs) to work directly with customers, internal stakeholders, and product teams to design, build, deploy, and scale high impact AI enabled systems.
- This role sits at the intersection of software engineering, AI product development, solution architecture, and customer delivery.
Key Responsibilities
- Work directly with users and technical stakeholders to understand workflows, data sources, pain points, and success criteria.
- Translate ambiguous operational requirements into clear technical designs and deployable solutions.
- Build production grade applications, APIs, integrations, data workflows, and AI powered features.
- Develop LLM, RAG, agentic, extraction, classification, and decision support workflows.
- Deploy and operate solutions using cloud platforms, containers, CI/CD, monitoring, logging, testing, and security controls.
- Debug issues across application, data, model, infrastructure, and integration layers.
- Scale successful pilots into production rollouts across multiple teams or customer environments.
- Identify reusable implementation patterns and convert customer feedback into actionable product requirements and playbooks.
Required Qualifications
- Strong software engineering experience in at least one major area: backend, full stack, data engineering, AI engineering, cloud infrastructure, or enterprise integrations.
- Demonstrated ability to build and ship working software in real operational environments.
- Proficiency in one or more modern programming languages: Python, TypeScript, JavaScript, Go, or Java.
- Practical experience with APIs, databases, authentication, deployment workflows, and production debugging.
- Strong problem solving ability in ambiguous and fast moving environments.
- Sound judgment when balancing speed, reliability, security, scalability, and maintainability.
- Excellent communication skills and confidence working directly with both technical and non technical stakeholders.
Relevant Team Capabilities
- Software and Product Engineering: Full stack applications, backend services, APIs, event driven systems, databases, enterprise integrations.
- AI and LLM Engineering: RAG, AI agents, tool calling, evaluations, prompt regression testing, model monitoring, fine tuning, inference optimization, AI red teaming.
- Infrastructure, DevOps, and Security: AWS, Azure, GCP, private cloud, Docker, Kubernetes, Terraform, CI/CD, observability, IAM, audit logging, secrets management.
- Data and Enterprise Integration: ETL/ELT, streaming, document ingestion, data quality, schema design, integrations with CRM, ERP, ticketing, SIEM/SOAR, email, document repositories.
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