Architect, Applied Science – AgentForce

About This Role

This role will define the technical vision and system design for AgentForce's AI capabilities, focusing on bridging cutting-edge model development with robust, scalable production infrastructure. You will architect the next generation of the AI platform, enabling trustworthy, high-performance AI systems across sales, service, marketing, and analytics.

Responsibilities

• Define the end-to-end architecture for AgentForce's model serving, inference orchestration, and agentic reasoning loops
• Make high-stakes technical decisions regarding "build vs. buy," model sizing, context window management, and retrieval-augmented generation (RAG) strategies
• Architect scalable pipelines for continuous learning (RLHF/RLAIF) that integrate seamlessly with production traffic without compromising latency or stability
• Design systems for multi-turn agent state management, memory persistence, and tool invocation (function calling)
• Own the end-to-end architectural design of AgentForce AI capabilities from product requirements through model design, system implementation, and production rollout
• Translate product use cases into concrete system architectures, including APIs, service contracts, and model interaction patterns
• Define reference architectures for AI-powered applications that standardize how products integrate with AgentForce models
• Partner with Product Engineering to ensure AI capabilities are designed for usability, reliability, and developer experience
• Translate abstract research concepts into concrete engineering specifications
• Lead the design of evaluation frameworks that move beyond academic benchmarks to measure real-world system performance

Requirements

• PhD or Master’s in Computer Science, AI, Machine Learning, or Distributed Systems
• 10+ years of technical experience, with a specific focus on deploying ML models at scale
• Proven experience acting as an Architect or Principal-level technical lead for large-scale AI or data platforms
• Experience designing and building production-grade AI-powered applications or platforms
• Experience defining public/internal APIs, SDKs, and service interfaces for ML/AI capabilities consumed by product teams
• Familiarity with frontend–backend–model interaction patterns for low-latency user-facing AI experiences
• Profound understanding of Transformer architectures, attention mechanisms, and the math behind LLMs
• Experience with high-performance inference serving (e.g., vLLM, TensorRT-LLM, TGI, Triton) and optimization techniques (quantization, LoRA adapters, paged attention)
• Strong background in designing distributed systems, microservices, and event-driven architectures (Kafka, gRPC, Kubernetes)
• Advanced proficiency in Python
• Ability to design for constraints: balancing model performance (accuracy) against system constraints (latency, throughput, COGS/compute costs)
• Experience designing architectures for "Agentic" workflows (planning, reasoning, tool use, memory)
• Familiarity with vector stores and search infrastructure (e.g., FAISS, Weaviate, Elasticsearch) for RAG implementations

Qualifications

• PhD or Master’s in Computer Science, AI, Machine Learning, or Distributed Systems
• 10+ years of technical experience, with a specific focus on deploying ML models at scale

Nice to Have

• Familiarity with C++ or CUDA is a strong plus
• Experience architecting platforms for Reinforcement Learning (RL) in production environments
• Ability to map product requirements to system architecture, model design, and infrastructure choices
• Strong intuition for user experience constraints (latency, streaming, partial results, fallbacks)
• Experience balancing feature velocity vs. platform stability
• Active contributor to open-source LLM infrastructure projects (e.g., Ray, LangChain, Hugging Face)
• Experience with safety guardrails and governance architectures for Enterprise AI

Skills

* Required skills

Benefits