We are looking for a Forward Deployment Engineer (FDE) – AI/GenAI who can work closely with customers to design, build, deploy, and scale production-ready AI solutions.
The role combines AI/GenAI engineering, solution architecture, customer engagement, and production deployment. You will take AI solutions from POC to production and ensure they are secure, scalable, reliable, and measurable.
Key Responsibilities
Design and deploy production-ready AI and GenAI solutions in customer environments.
Build Agentic AI applications using RAG, LLMs, tool/function calling, orchestration, and multi-step workflows.
Understand customer requirements and convert business problems into practical technical solutions.
Work directly with customer engineering and business teams during discovery, implementation, and rollout.
Take AI solutions from POC to production and drive adoption.
Design reliable systems with proper monitoring, logging, error handling, retries, fallbacks, and safe failure mechanisms.
Optimize AI applications for performance, latency, scalability, and cost.
Establish deployment, evaluation, and monitoring frameworks for AI systems.
Collaborate with AI Engineers, Platform Engineers, and Product/Research teams.
Communicate technical architecture, risks, trade-offs, and progress to technical and non-technical stakeholders.
Ensure smooth handover of deployed solutions to customer/platform teams with proper documentation and operational support plans.
Mentor engineers on production-grade AI development and deployment practices.
Required Skills
5+ years of experience in Software Engineering / AI Engineering / Solutions Engineering / Technical Consulting.
Strong experience with Python and modern software development practices.
Hands-on experience building and deploying GenAI/LLM applications.
Strong understanding of RAG, Agentic AI, LLM orchestration, prompt engineering, and tool/function calling.
Experience with APIs, microservices, cloud platforms, and production deployments.
Experience working with customers or cross-functional teams in a delivery environment.
Strong understanding of system design, scalability, reliability, monitoring, and observability.
Ability to convert ambiguous business requirements into practical technical architectures.
Good to Have
Experience with Azure / AWS / GCP.
Experience with vector databases and AI frameworks.
Experience with FastAPI, LangChain, LlamaIndex, Semantic Kernel or similar frameworks.
Experience with LLM evaluation and AI observability.
Experience with enterprise AI deployments.
Experience mentoring engineers or leading technical delivery.