Remote Forward Deployed AI Engineer (FDE)

Innovative Systems Group

5-7 Years

Philippines

Posted 3 hours ago
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Job Description

Job Description: Forward Deployed AI Engineer (FDE) 100% WFH Setup Night Shift

Position Overview

We are seeking a highly skilled Forward Deployed AI Engineer (FDE) to work directly with enterprise clients, designing, building, and deploying AI-powered solutions that solve complex business challenges. This role combines deep technical expertise in Generative AI with strong client-facing consulting skills.

As a Forward Deployed AI Engineer, you will bridge the gap between cutting-edge AI technologies and real-world business applications by integrating large language models (LLMs), AI agents, and workflow automations into client environments. You will work closely with customers to understand their requirements, develop custom AI solutions, deploy them into production, and provide continuous optimization and feedback to internal product and research teams.

Key Responsibilities

AI Solution Design & Deployment

Design, build, and deploy AI-powered applications, copilots, and autonomous agents tailored to client business needs.
Develop production-ready Generative AI solutions leveraging leading LLM platforms such as OpenAI, Claude, Gemini, Azure OpenAI, and other foundation models.
Lead AI projects through the full lifecycle, from discovery and prototyping to production deployment and ongoing optimization.

On-Site Integration

Integrate large language models, AI agents, and orchestration frameworks into enterprise environments.
Connect AI systems with legacy applications, internal databases, APIs, CRMs, ERPs, and other enterprise software platforms.
Build scalable data pipelines and retrieval systems to support AI applications across multiple business functions.

Custom Tuning & Optimization

Fine-tune prompts, workflows, and model configurations to improve output quality and business relevance.
Troubleshoot non-deterministic model behaviors and implement robust evaluation frameworks.
Develop testing, monitoring, and performance measurement strategies for AI applications in production environments.
Implement Retrieval-Augmented Generation (RAG) pipelines and vector database architectures to enhance AI accuracy and reliability.

Client Engagement & Advisory

Partner directly with business and technical stakeholders to gather requirements and translate business challenges into scalable AI solutions.
Present technical concepts, project updates, and AI recommendations to both technical and non-technical audiences.
Manage stakeholder expectations while balancing technical feasibility, security, and business objectives.

Product Feedback & Innovation

Act as the voice of the customer by providing real-world insights to product, engineering, and research teams.
Identify recurring challenges, feature opportunities, and best practices from client deployments.
Contribute to the evolution of internal AI platforms, agent frameworks, and deployment methodologies.

Required Qualifications

Bachelor's degree in Computer Science, Engineering, Data Science, Information Technology, or a related technical discipline.
5+ years of software engineering experience building and deploying production-grade applications.
Strong hands-on experience with Generative AI technologies and Large Language Models (LLMs) including OpenAI, Claude, Gemini, Azure OpenAI, or similar platforms.
Experience developing AI agents, copilots, chatbots, and workflow automation solutions.
Advanced proficiency in Python and experience with modern AI frameworks such as:
LangChain
LangGraph
LlamaIndex
Similar agent orchestration frameworks
Experience integrating AI solutions with enterprise systems through:
REST APIs
Databases
CRM platforms
ERP systems
Third-party business applications
Experience deploying AI applications into cloud environments such as AWS, GCP, or Azure.
Strong understanding of software engineering best practices, including testing, monitoring, CI/CD, and system reliability.
Excellent communication, presentation, and stakeholder management skills.

Preferred Qualifications

Experience with Retrieval-Augmented Generation (RAG) architectures.
Knowledge of vector databases such as Pinecone, Weaviate, Chroma, Milvus, or Azure AI Search.
Familiarity with Model Context Protocol (MCP) and AI tool integration patterns.
Experience building evaluation frameworks and AI observability solutions.
Understanding of prompt engineering techniques and LLM optimization strategies.
Background in machine learning operations (MLOps) and AI governance.
Experience working in consulting, customer-facing engineering, or solution architecture roles.

Required Technical Skills

AI & Machine Learning