Forward Deployed ML Engineer
- Build and deploy LLM-powered solutions (agents, RAG pipelines, tools integration) directly with enterprise customers.
- Collaborate with cross-functional teams including Product Managers, Solutions Architects, and Customer Success to understand client requirements and deliver tailored AI solutions.
- Iterate quickly on customer-facing use cases, incorporating feedback loops to improve model and system performance.
- Support customers in implementation, fine-tuning, and integration of LLMs in production environments.
- Share insights from deployments with core engineering and product teams to influence roadmap and feature prioritization.
- Occasionally travel to customer sites or participate in virtual workshops and implementation sessions.
- Work closely with enterprise customers to understand their workflows and define solution architectures involving AI agents, LLMs, and retrieval systems.
- Translate real-world customer problems into deployable AI workflows using planning, memory, retrieval, and tool integration.
- Implement and optimize agentic AI systems using LLMs and tool orchestration frameworks (e.g., LangChain, RAG pipelines).
- Conduct prompt engineering, performance evaluation, and fine-tuning to improve solution effectiveness and reliability.
- Collaborate with product and platform teams to identify reusable patterns and influence roadmap based on deployment learnings.
- Serve as a technical point of contact during deployments and early adoption phases.
- Capture feedback from real-world usage and help prioritize improvements and fixes.
- 3+ years of experience in ML, NLP, or software engineering, with recent exposure to LLMs and agentic workflows.
- Strong programming skills in Python and relevant libraries (Transformers, LangChain, FastAPI, etc.).
- Experience with cloud infrastructure and deploying ML models in real-world settings.
- Ability to work directly with customers and translate business problems into AI solutions.
- Familiarity with vector databases, retrieval systems, and prompt engineering best practices.
- Prior experience in a forward-deployed, customer-facing, or consulting engineering role.
- Exposure to compliance and enterprise deployment constraints (e.g., PII, data governance).
- Background in MLOps, data pipelines, or orchestration tools (Ray, Airflow, etc.).
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