AI Solutions Architect (LLMs & AI Agents)

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

Who You Are:

Experience & Qualifications:

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

Soft Skills & Cultural Fit:

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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