About Kindsight:
Kindsight builds technology that helps fundraisers make a difference. For decades, Kindsight has supported the education, healthcare, and nonprofit sectors with fundraising tools and the largest charitable giving database on the market. And as the giving sector evolves, so does Kindsight. As the leader in fundraising intelligence, Kindsight leverages real-time data and AI to help thousands of organizations around the world identify, manage, and engage with donors - at any scale. With purpose-built CRMs that corral all of that donor information and campaign tracking into one place, donor prospect research tools that offer proactive insights and real-time donor intel, and generative AI that creates personalized, meaningful content drafts at scale, Kindsight’s product suite is truly changing the game for donor fundraising.
Position Summary:
We are seeking an Intermediate Fullstack AI Platform Engineer to help build AI agents and agent-backed application workflows on AWS. This role will focus on agentic application development, including agent logic, tool calling, structured outputs, retrieval workflows, prompt and configuration handling, evaluation, and integration with backend and frontend services.
The engineer will work with Python, React/TypeScript, Amazon Bedrock, Amazon Bedrock AgentCore, Strands, LangGraph, LangChain, MCP, A2A-style agent communication patterns, vector databases, and related AI engineering tools. This is a hands-on engineering role for someone who can implement scoped agent features, integrate agents with APIs and tools, build supporting backend/frontend functionality, and work within platform patterns defined by senior engineers.
The role does not require deep ownership of AWS infrastructure architecture, but the engineer should be comfortable working in an AWS-based environment and contributing to cloud-native services, tests, deployments, and operational improvements.
What You’ll Do:
- Build and maintain AI agents and agent-backed workflows using frameworks such as Strands, LangGraph, LangChain, or comparable tools.
- Implement agent logic for task planning, tool selection, tool execution, response handling, and multi-step workflows.
- Build tool-calling integrations between agents and internal APIs, external services, databases, enterprise systems, and retrieval sources.
- Implement structured output patterns, including JSON schemas, validation, retry handling, and response normalization.
- Work with Amazon Bedrock and related AWS AI services for model invocation, inference configuration, and response handling.
- Support AgentCore-based runtime patterns, including session handling, runtime invocation, memory-aware workflows, and agent execution metadata.
- Build retrieval-augmented generation workflows using vector databases, embeddings, document chunks, metadata filters, and relevance tuning.
- Contribute to MCP-based tool/server integrations and A2A-style agent communication patterns where applicable.
- Implement prompt and configuration handling for agent behavior, including prompt templates, model parameters, feature flags, and tool configuration.
- Build Python backend services that support agent execution, API integration, job processing, session state, and response persistence.
- Build React/TypeScript frontend screens for testing agents, reviewing outputs, managing configuration, viewing evaluations, and monitoring execution status.
- Write automated tests for agent behavior, tool calls, structured outputs, retrieval workflows, backend APIs, and frontend flows.
- Support evaluation workflows for AI agents, including test datasets, expected outputs, regression checks, and result review.
- Help troubleshoot agent behavior, tool failures, retrieval quality issues, malformed outputs, latency problems, and integration errors.
- Work with senior engineers to implement features within established AWS, CI/CD, security, and observability patterns.
What We’re Looking For:
- 3–5 years of experience as a fullstack, backend, AI application, or platform-adjacent engineer.
- 1–3 years of Python backend engineering experience.
- Experience building or integrating AI, LLM, or agent-based applications.
- Experience with agentic frameworks such as Strands, LangGraph, LangChain, Semantic Kernel, AutoGen, or comparable tools.
- Familiarity with agentic application patterns such as tool calling, planning, structured outputs, multi-turn workflows, memory, retrieval, and evaluation.
- Experience defining or consuming structured outputs using JSON, JSON Schema, Pydantic, OpenAPI, or similar validation approaches.
- Experience integrating applications with REST APIs, internal services, external tools, or enterprise systems.
- Exposure to MCP, tool-server patterns, or protocol-based tool integration is preferred.
- Exposure to A2A-style agent communication, multi-agent workflows, or agent handoff patterns is preferred.
- Experience or practical exposure to vector databases, embeddings, semantic search, document chunking, metadata filtering, or RAG workflows.
- Experience with Amazon Bedrock or comparable managed LLM services is preferred.
- Exposure to Amazon Bedrock AgentCore or similar managed agent runtime capabilities is preferred.
- Experience with React and TypeScript.
- Experience building frontend screens with forms, tables, filters, validation, loading states, error handling, and API integration.
- Experience building and consuming REST APIs.
- Working familiarity with AWS services such as Lambda, API Gateway, SQS, DynamoDB, S3, CloudWatch, IAM, Step Functions, or Cognito.
- Basic familiarity with infrastructure-as-code, CI/CD pipelines, automated testing, and environment-based deployments.
- Ability to implement features from technical designs and collaborate with senior engineers on architecture, infrastructure, and production patterns.
- Strong debugging, communication, and documentation skills.
- Nice to have: experience with GitLab CI/CD, Cognito/OIDC, evaluation frameworks, observability tooling, multi-tenant systems, or regulated enterprise environments.
Compensation Range: $110, 000 - $150, 000 CAD OTE annually, based on experience, market benchmarks and role complexity. We aim to offer fair, competitive pay that reflects your skills and the market.
This advertised position is for an existing vacancy at Kindsight. At Kindsight, we’re proud to be a place where everyone belongs and has an equal opportunity to contribute, thrive and grow. We hire based on skills, potential, and impact, and we believe our differences fuel innovation. We welcome all individuals and do not discriminate on the basis of gender identity and expression, race, ethnicity, disability, sexual orientation, colour, religion, creed, gender, national origin, age, marital status, pregnancy, sex, citizenship, education, languages spoken or veteran status. We’re building a workplace where everyone has the opportunity to do meaningful work and make a difference.
We leverage artificial intelligence (AI) tools to support certain aspects of our recruitment process. These tools may help with resume screening, drafting job descriptions, creating interview questions and occasionally identifying potential candidates. All hiring decisions are made by our people, not AI. Our intent is to use AI thoughtfully to streamline administrative tasks, improve the candidate experience and support fair, unbiased hiring practices consistent with industry standards.
