Coder
AI software development
SeniorSoftwareEngineer(AISolutions)
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Software Engineer (AI Solutions) at Coder. Skills: AI Solutions, LLM-based applications, TypeScript/Node.js, Python, AWS, Data Integration/Pipelines, RAG, Prompt Engineering. Design and implement AI workflows on top of existing data pipelines (product feedback extraction, customer update generation, onboarding plans, win/loss summaries, CRM enrichment). Extend and refine the current TypeScript-based agent: tooling, tool schemas, routing logic, error handling, and observability”
What You'll Achieve.
Keep the system accurate, observable, and cost-aware; Expose workflows to end users; Accelerate innovation while maintaining control and compliance; Improve productivity, cut cloud costs, and reduce data risks; Developers transition to AI at their own pace using their own tools; Platform and security teams can govern, audit, and manage a great developer experience at scale
Industry & Context.
Turn scattered GTM data into clear answers and usable workflows; Improve transcript and account matching; Optimize Lambda-based data processing jobs for cost, reliability, and performance; Refactoring toward higher quality (testing, structure, observability)
Ability to travel up to 20%
What They're Looking For.
Must Have
5+ years in software engineering, 1-2+ years building LLM-based applications or agents, TypeScript/Node.js or Python for backend and data processing, LLM tool calling/agents (custom frameworks, LangChain, or equivalent), RAG expertise: chunking, metadata schemas, retrieval, relevancy, and evaluation, AWS: Lambda, S3, IAM, Data integration/pipelines: consuming APIs and webhooks (Salesforce, Slack, Zendesk, Zapier, etc.), Designing JSON schemas, pre-aggregated summaries, and metadata models for query, Prompt engineering and evaluation for business workflows (accuracy, reliability, user trust)
Nice to Have
Bedrock is a plus, 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
What You'll Do.
Design and implement AI workflows on top of existing data pipelines (product feedback extraction, customer update generation, onboarding plans, win/loss summaries, CRM enrichment), Extend and refine the current TypeScript-based agent: tooling, tool schemas, routing logic, error handling, and observability, Improve transcript and account matching across Zoom, Granola, and Salesforce using entity resolution, heuristics, and/or LLM-assisted matching, Integrate new data sources (Slack, Zendesk, Google Drive, Nexus/telemetry, email) into the existing AWS stack, Define and consume pre-aggregated account/opportunity summaries in S3 for fast, reliable query, Optimize Lambda-based data processing jobs for cost, reliability, and performance, Iterate on model strategy: cheap routing (e.
, Claude Haiku) vs.
higher-quality response models (e.
, Claude Sonnet/Opus), Evaluate prompts, tool selection quality, and response accuracy with clear metrics, Contribute to future UI/UX (Slack bot flows, simple web UI/dashboards) to expose workflows to end users.
How You'll Work.
Team & Collaboration
Partner with GTM leaders, product, and infra to test, measure, and refine; Collaborate with GTM stakeholders (Sales, SE, CS, Product, Marketing) to define, test, and refine AI-assisted workflows; Partner with infra engineering (Terraform, Kubernetes) to ensure deployment, security, and observability are production-ready
Communication Scope
Communicate with GTM stakeholders; Communicate with product and infra teams
Full Job Description
You’ll build the AI behind Animus, our internal agent for revenue and customer intelligence. Your work turns scattered GTM data into clear answers and usable workflows for Sales, Success, Product, and Marketing. You design, ship, and improve AI workflows on a proven AWS and TypeScript base. You partner with GTM leaders, product, and infra to test, measure, and refine while keeping the system accurate, observable, and cost-aware. What you’ll do here - Design and implement AI workflows on top of existing data pipelines (product feedback extraction, customer update generation, onboarding plans, win/loss summaries, CRM enrichment) - Extend and refine the current TypeScript-based agent: tooling, tool schemas, routing logic, error handling, and observability - Improve transcript and account matching across Zoom, Granola, and Salesforce using entity resolution, heuristics, and/or LLM-assisted matching - Integrate new data sources (Slack, Zendesk, Google Drive, Nexus/telemetry, email) into the existing AWS stack - Define and consume pre-aggregated account/opportunity summaries in S3 for fast, reliable query - Optimize Lambda-based data processing jobs for cost, reliability, and performance - Iterate on model strategy: cheap routing (e.g., Claude Haiku) vs. higher-quality response models (e.g., Claude Sonnet/Opus) - Evaluate prompts, tool selection quality, and response accuracy with clear metrics - Collaborate with GTM stakeholders (Sales, SE, CS, Product, Marketing) to define, test, and refine AI-assisted workflows - Partner with infra engineering (Terraform, Kubernetes) to ensure deployment, security, and observability are production-ready - Contribute to future UI/UX (Slack bot flows, simple web UI/dashboards) to expose workflows to end users What we’re looking for - 5+ years in software engineering, including 1-2+ years building LLM-based applications or agents - Strong experience in TypeScript/Node.js (ideal) or Python for backend and data processing - Hands-on experie
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