Key skills
PythonBashAIMLLLMRAGCI/CDCommunication
About this role
Job Title:
AI Forward Deployed Engineer
Location:
Remote
Duration:
12 Month Contract
Position Overview:
The AI Forward Deployed Engineer (FDE) is responsible for deploying, integrating, and operating AI solutions in real customer production environments. This role sits at the intersection of data science, engineering, and operations, translating AI capabilities into reliable, secure, and high-performing systems. The ideal candidate is hands-on, customer-facing, and excels at rapid problem-solving in ambiguous, high-stakes environments.
Key Responsibilities
Lead customer-facing deployments of AI solutions from initial integration through production launch and stabilization.
Integrate AI systems into existing customer architectures, including applications, APIs, data pipelines, and infrastructure
Design and support AI inference architectures optimized for scalability, resiliency, latency, and cost.
Troubleshoot complex production issues across model, application, infrastructure, and data layers.
Build automation and scripts to accelerate deployments, testing, diagnostics, and operational workflows.
Implement and tune observability, including monitoring, logging, tracing, and alerting for AI systems.
Perform performance tuning and capacity planning for inference workloads.
Support deployments across cloud, on-prem, and hybrid environments, adapting solutions to customer constraints.
Apply security-aware implementation practices, including identity and access management, secrets handling, and data protection.
Act as a technical bridge between data science, engineering, and operations teams, ensuring smooth handoffs and shared understanding.
Serve as a technical escalation point during deployments and early production operations.
Document deployment patterns, operational runbooks, and best practices to improve repeatability and reliability.
Provide feedback from real-world deployments to inform product and platform improvements.
Required Qualifications
Strong experience deploying and operating production systems, ideally with AI or ML components.
Proven ability to troubleshoot and resolve complex system issues under time pressure.
Experience designing or supporting AI inference pipelines.
Proficiency in automation and scripting (e.g., Python, Bash, or similar).
Solid understanding of observability and performance tuning in distributed systems.
Experience working across cloud, on-prem, or hybrid infrastructure environments.
Working knowledge of security best practices for production systems.
Strong communication skills and comfort working directly with customers and cross-functional teams.
Preferred Qualifications
Experience with LLM-based systems, RAG architectures, or agent-based workflows.
Familiarity with containerization, orchestration, and CI/CD pipelines.
Background in SRE, platform engineering, DevOps, or systems engineering.
Experience operating in fast-moving or ambiguous environments.
Prior customer-facing technical or forward-deployed role experience.
AI Forward Deployed Engineer
Location:
Remote
Duration:
12 Month Contract
Position Overview:
The AI Forward Deployed Engineer (FDE) is responsible for deploying, integrating, and operating AI solutions in real customer production environments. This role sits at the intersection of data science, engineering, and operations, translating AI capabilities into reliable, secure, and high-performing systems. The ideal candidate is hands-on, customer-facing, and excels at rapid problem-solving in ambiguous, high-stakes environments.
Key Responsibilities
Lead customer-facing deployments of AI solutions from initial integration through production launch and stabilization.
Integrate AI systems into existing customer architectures, including applications, APIs, data pipelines, and infrastructure
Design and support AI inference architectures optimized for scalability, resiliency, latency, and cost.
Troubleshoot complex production issues across model, application, infrastructure, and data layers.
Build automation and scripts to accelerate deployments, testing, diagnostics, and operational workflows.
Implement and tune observability, including monitoring, logging, tracing, and alerting for AI systems.
Perform performance tuning and capacity planning for inference workloads.
Support deployments across cloud, on-prem, and hybrid environments, adapting solutions to customer constraints.
Apply security-aware implementation practices, including identity and access management, secrets handling, and data protection.
Act as a technical bridge between data science, engineering, and operations teams, ensuring smooth handoffs and shared understanding.
Serve as a technical escalation point during deployments and early production operations.
Document deployment patterns, operational runbooks, and best practices to improve repeatability and reliability.
Provide feedback from real-world deployments to inform product and platform improvements.
Required Qualifications
Strong experience deploying and operating production systems, ideally with AI or ML components.
Proven ability to troubleshoot and resolve complex system issues under time pressure.
Experience designing or supporting AI inference pipelines.
Proficiency in automation and scripting (e.g., Python, Bash, or similar).
Solid understanding of observability and performance tuning in distributed systems.
Experience working across cloud, on-prem, or hybrid infrastructure environments.
Working knowledge of security best practices for production systems.
Strong communication skills and comfort working directly with customers and cross-functional teams.
Preferred Qualifications
Experience with LLM-based systems, RAG architectures, or agent-based workflows.
Familiarity with containerization, orchestration, and CI/CD pipelines.
Background in SRE, platform engineering, DevOps, or systems engineering.
Experience operating in fast-moving or ambiguous environments.
Prior customer-facing technical or forward-deployed role experience.