AI Solutions Architect (LLMs & AI Agents)
Indexed description
We need someone who can go deep technically — designing agentic architectures, selecting frameworks, writing code — and then step back to own the "what" and "why": shaping the roadmap, defining success metrics, and aligning stakeholders. You are equally comfortable whiteboarding a RAG pipeline as you are presenting a product vision to sales leadership.
What You'll Do
- Design and implement AI agent architectures for sales workflows — autonomous agents for lead qualification, deal coaching, proposal generation, competitive intelligence, and pipeline analytics
- Own the product backlog and roadmap for AI agent capabilities: define priorities, write user stories, set acceptance criteria, and make trade-off decisions based on business impact
- Architect multi-agent orchestration systems using modern frameworks (LangChain, AutoGen, CrewAI, Semantic Kernel) with production-grade reliability, observability, and scalability
- Build integrations between AI agents and sales platforms (Salesforce, enablement tools, data lakes) ensuring real-time, contextual intelligence reaches sellers when they need it
- Define and track success metrics (adoption, pipeline velocity, win-rate impact, time saved) — use data to iterate rapidly and demonstrate ROI to stakeholders
- Establish technical standards for prompt engineering, agent guardrails, memory management, tool-use patterns, and responsible AI deployment
- Collaborate with sales leadership and revenue operations to identify high-value use cases and translate business needs into technical solutions
- Lead sprint planning, backlog grooming, and release management — ensuring the engineering team delivers incrementally against a clear product vision
- Bachelor's or Master's degree in Computer Science, AI/ML, Software Engineering, or related field
- 5+ years in solution architecture or senior software engineering, with 2+ years focused on AI/ML or agentic systems
- Proven experience as a product owner or technical product manager — owning backlogs, defining roadmaps, and delivering business outcomes
- Expert-level Python and hands-on experience with AI/ML frameworks (LangChain, AutoGen, CrewAI, Semantic Kernel, or equivalent)
- Strong knowledge of LLMs, RAG architectures, vector databases, embedding models, and prompt engineering
- Experience with cloud platforms (AWS, Azure, or GCP) and CI/CD pipelines for ML systems
- Familiarity with CRM platforms (Salesforce preferred) and sales technology ecosystems
- Builder mindset — thrives when wearing both the architect and product owner hats; moves fluidly between code and strategy
- Strong communicator — articulates technical decisions to business stakeholders and translates sales needs into engineering priorities
- Data-driven — obsessed with measuring impact, running experiments, and letting results guide product direction
- Collaborative — builds trust with engineering, data science, and sales teams; leads by influence rather than authority
- Fluent in English (mandatory)
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