Senior Software Engineer - AI and Automation, Data & Analytics
USA, USA time zones onlyFull-TimeSenior
Salary154445 - 185334 USD per year
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Job Details
- Experience
- 5+ years
- Required Skills
- Node.jsPythonSQLJavascriptSalesforceRESTful APIsMicroservicesHubSpotSlackBigQueryAWS LambdaLangChain
Requirements
- 5+ years of software engineering experience, including backend development and systems integration work
- Strong proficiency in Python (preferred) or Javascript/Node.js
- Hands-on experience with LLM APIs (OpenAI, Anthropic Claude, or similar) and orchestration libraries (LangChain, LlamaIndex, Anthropic MCP, Semantic Kernel, etc.)
- Comfortable building internal APIs, microservices, or serverless systems (GCP Cloud Functions, Cloud Run, AWS Lambda, or similar)
- Familiarity with SQL and data warehouses (BigQuery preferred)—you understand how to query, structure, and pipeline data for AI workflows
- Experience with authentication patterns, secure API handling, rate limiting, and workflow automation
- Proven ability to deliver AI-powered features in production environments
- Strong problem selector who can identify high-leverage initiatives and push back on low-impact requests
- Thrives in ambiguous, fast-moving projects; able to balance experimentation with engineering rigor
- Clear technical communicator—you can explain complex systems in simple terms and collaborate effectively with product and data stakeholders
- Comfort with autonomy—you identify the right questions, structure unstructured problems, and drive work independently
Responsibilities
- Own end-to-end development of agentic and AI-integrated workflows: design, implementation, testing, deployment, and maintenance
- Build modular, composable agentic systems using frameworks like LangChain, CrewAI, Anthropic MCP, or similar orchestration libraries
- Develop "agentic skills" for SDR and CSM teams—reusable capabilities that agents can invoke across interfaces
- Implement observability and feedback loops: logging, performance metrics, prompt iteration, and model evaluation
- Build servers, CLIs, APIs, and microservices that connect AI models to business systems: Salesforce, BigQuery, Slack, HubSpot, email, calendars, analytics tools
- Architect data flows that enable retrieval-augmented generation (RAG): connecting LLMs to internal knowledge bases, customer data in BigQuery, Salesforce data, and real-time business context
- Build serverless or containerized services (GCP Cloud Functions, Cloud Run, or similar) that scale with usage and integrate with Grafana's cloud infrastructure
- Scope high-impact automation problems autonomously by shadowing Sales, Customer Success, and Marketing teams to identify efficiency gaps
- Design and deploy automation workflows using tools like n8n, Zapier, Prefect, or custom orchestration platforms
- Establish and champion governance and compliance standards to AI workflows, including access controls, audit trails, and human-in-the-loop escalation path
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