Senior Software Engineer (AI Solutions)

Requirements and responsibilities

Readable role content extracted into sections for faster review.

Details

Implement and iterate on AI workflows on top of existing data pipelines — product feedback extraction, customer update generation, onboarding plans, win/loss summaries, CRM enrichment
Extend and improve the FastAPI + LangGraph agentic loop: tool definitions, routing logic, prompt strategy, error handling, and observability
Integrate new data sources (Zendesk, Google Drive, telemetry, email) into the existing AWS Lambda pipeline architecture
Define and consume pre-aggregated account/opportunity summaries in S3 for fast, reliable structured query
Optimize Lambda-based data processing jobs for cost, reliability, and performance
Iterate on model strategy: cost-efficient routing (e.g., Claude Haiku) vs. higher-quality responses (e.g., Claude Sonnet/Opus) — knowing when to spend tokens and when not to
Evaluate prompts, tool selection quality, and response accuracy with clear metrics; build toward measurable quality in an agentic system
Collaborate with GTM stakeholders (Sales, SE, CS, Product, Marketing) to define, test, and refine AI-assisted workflows
Contribute to the web UI layer (React/TypeScript) as needed — leveraging agent tooling to execute frontend work efficiently
Contribute to the long-term evolution of the integration layer, including emerging patterns like Salesforce MCP for structured CRM access
3+ years in software engineering, including 1+ years building LLM-based applications or agents
Genuine fluency with AI-native development (AI-DLC): using agents and sub-agents to write, review, and iterate on code as standard practice — not an experiment
Strong Python for backend and data processing; FastAPI or equivalent async framework preferred
Hands-on experience with LLM tool calling and agentic loops — LangGraph, LangChain, N8N, or equivalent
Practical RAG expertise: chunking, metadata schemas, retrieval quality, and evaluation
Cloud infrastructure experience is a strong plus — AWS (Lambda, S3, Bedrock) preferred, but comfort with Railway, GCP, or similar and solid CI/CD experience is equally valued
Background in data integration and pipelines: consuming APIs and webhooks (Salesforce, Zoom, Slack, Zendesk, etc.)
Skill in designing JSON schemas, pre-aggregated summaries, and metadata models for structured query
Familiarity with Salesforce data structures (accounts, opportunities, leads) or willingness to ramp quickly
Experience with prompt engineering and evaluation for business workflows — accuracy, reliability, and user trust in AI-generated outputs
Comfort with modern JavaScript frameworks (React/TypeScript preferred) for contributing to frontend work
Openness to figuring things out together — we are actively learning how to integrate fully agentic workflows into a team environment and need someone who embraces that process, not someone who needs it fully defined before they start
Ability to travel up to 20%
(Tacos? If you need an ice-breaker, ask how we say thanks by giving tacos!)
Amazon Bedrock knowledge bases.
Terraform and/or Kubernetes.
Slack bot development and slash commands.
CRM enrichment, sales tooling, or GTM analytics.
GDPR/data privacy considerations for AI systems.
Prior work on sales/revops intelligence tools, conversation intelligence (Zoom/Granola/Gong‑style), or human‑in‑the‑loop review flows.

Our use of AI in hiring

Ashby helps us review inbound applications by surfacing candidates who best match the role requirements we've defined. This tool does not make hiring decisions - it helps our team prioritize which applications to review first.
Granola takes notes during our interview calls so our team can focus on the conversation with you.

Similar roles