Lead the design, development, and deployment of generative and agentic AI solutions for enterprise clients, integrating large language models, APIs, and enterprise data into production systems.
Compensation
The annual salary range for this position is $113,000.00-$188,000.00.
Responsibilities
Translate business and mission needs into technical designs and prototypes
Build and integrate AI solutions using large language models and enterprise data
Develop agentic workflows with tool use, structured outputs, and human-in-the-loop patterns
Implement Model Context Protocol and agent-to-agent communication
Deploy RAG solutions with vector search and embeddings
Requirements
Bachelor’s degree in Computer Science or related field
Nice to have: Bachelor’s degree
Nice to have: prior experience as Forward Deployed Engineer
Own and drive end-to-end architecture, solution design, and delivery of agentic AI solutions for enterprise clients across diverse industries.
Design and build reusable agentic accelerators and reference architectures aligned with the Intelligence Platform that can be deployed across multiple clients in a fixed-price format.
Translate ambiguous business problems into clear agentic solution designs that align with the phData Intelligence Platform, methodologies, standards, and best practices.
Architect production-grade agents emphasizing reliability, scalability, security, observability, and evaluation and guardrail strategies for safe, accurate performance.
Seeking a Staff AI/ML Engineer to drive innovation and evolution of Conversational AI solutions with a focus on Generative AI systems at global scale, specifically Agentic Virtual Agents.
Responsibilities
Own technical direction: define the architecture and roadmap for our agentic platform (planning, tool use, memory, orchestration, multi-agent collaboration).
Build the evaluation foundation for non-determistic systems, including offline and online evals, LLM-as-judge, simulation and regression testing, and production quality monitoring.
Improve reliability, safety and guardrails, latency and cost (model routing, caching, distillation, small-model strategies) at global scale.
Extend agents to more complex, multi-step, tool-using workflows and real-time voice and multimodal experiences.
Turn emerging research into validated production capabilities.
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