Uses Cursor and LLM APIs to build GenAI apps and agentic workflows—directly tied to vibe coding with AI assistants and rapid prototyping.
About the Role
Lead end-to-end design, build, and deployment of production-grade GenAI solutions as a hands-on Forward Deployed Engineer working directly with clients. Build LLM-driven applications, RAG systems, and agentic workflows while creating reusable accelerators and enabling large-scale AI adoption.
Job Description
Role
Manager — Forward Deployed Engineer (AI & Data). Responsible for designing, building, and deploying production-grade GenAI and AI/ML solutions directly with clients. Focus on building LLM-driven applications, RAG systems, and agentic workflows and delivering measurable business value while creating reusable accelerators and reference architectures.
Key Responsibilities
- Lead end-to-end delivery at strategic customers: discovery, design, development, and production deployment.
- Translate complex business problems into executable GenAI solutions in partnership with client business and engineering teams.
- Build LLM-powered applications (copilots, assistants), RAG systems, and agentic workflows; integrate with enterprise data platforms, APIs, and external systems.
- Optimize solutions for latency, cost, accuracy, scalability, security, and compliance.
- Convert proofs-of-concept into production-grade, enterprise-ready platforms through rapid iteration.
- Architect scalable, reliable, and observable systems using cloud-native patterns.
- Create reusable accelerators, reference implementations, and best practices for enterprise GenAI adoption.
- Act as a trusted technical advisor to senior and executive client stakeholders.
Requirements
Mandatory
- 9+ years of experience in software engineering, data engineering, or AI/ML.
- Strong hands-on experience building, deploying, and operating production-grade systems.
- Expert-level proficiency in Python for large-scale, high-performance applications.
- Solid foundation in distributed systems, microservices, APIs, and cloud-native architectures.
- Proven delivery experience in client-facing and high-ambiguity environments.
- Excellent communication skills with technical teams and business stakeholders; client experience required.
- Demonstrated ability to independently own and deliver complex, high-impact initiatives.
Desired / Preferred
- Hands-on experience with LLM APIs, prompt engineering, RAG architectures, and vector databases.
- Experience with agentic workflows and orchestration frameworks.
- Exposure to AI ecosystem tools such as OpenAI, Claude, Cursor, Palantir, and hyperscalers (Azure, AWS, GCP).
- Experience with DevOps, CI/CD pipelines, infrastructure automation, MLOps practices, and model monitoring.
- Consulting, global delivery, or field engineering experience with direct client exposure; prior FDE/solutions/field engineer background.
- Experience building 0→1 AI or data products; frontend/UI exposure for AI-driven applications.
What This Role Is Not
- Not a people management role.
- Not a delivery governance or program management role.
- Not limited to architecture reviews — expected to build and ship.
Attributes
- Deep technical expertise and a builder mindset; senior engineer who codes regularly.
- Comfortable in ambiguous, client-facing environments and able to learn quickly.