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 a Senior Fullstack AI Platform Engineer to design, build, deploy and operate production AI agent services on AWS. The role spans Python backend services, agentic application frameworks, Amazon Bedrock, Amazon Bedrock AgentCore, AWS serverless infrastructure, CI/CD, observability and enterprise system integrations.
The engineer will be expected to build platform capabilities end to end. This includes backend APIs, agent runtime integrations, infrastructure as code and React/TypeScript interfaces for internal platform users. The role is hands-on and engineering-heavy, with an emphasis on production backend systems, cloud infrastructure, AI/LLM applications and frontend development.
What You’ll Do:
- Design, implement, and maintain production AI agents and agent-backed backend services.
- Build agentic applications using frameworks such as Strands, LangGraph, LangChain, or comparable agent frameworks.
- Integrate agent services with Amazon Bedrock for model invocation, inference configuration, response handling, and model selection.
- Deploy and operate agent workloads using AWS-native runtime and serverless patterns, including Amazon Bedrock AgentCore where applicable.
- Build Python backend services for request validation, job orchestration, runtime dispatch, session handling, response persistence, and error handling.
- Implement integrations between AI agents, internal APIs, enterprise systems, external applications, data stores, and tool services.
- Develop reusable patterns for tool calling, multi-turn conversations, memory, prompt/configuration separation, runtime configuration, and evaluation.
- Build infrastructure using AWS CDK, preferably in Python.
- Work with AWS services such as Lambda, API Gateway, SQS, DynamoDB, S3, IAM, KMS, CloudWatch, Step Functions, and Cognito.
- Implement CI/CD pipelines for testing, packaging, deployment, environment promotion, and rollback.
- Add observability for agent services, including structured logs, metrics, traces, alarms, latency monitoring, token usage, and failure tracking.
- Build automated testing and evaluation workflows for AI agents, including regression datasets, deterministic checks, and model-based scoring where appropriate.
- Support production operations, including debugging, incident response, performance tuning, cost controls, and reliability improvements.
- Create reusable templates, libraries, and deployment patterns that make it easier to onboard future AI agents and use cases.
- Collaborate with backend, platform, security, DevOps, and product teams to deliver secure and maintainable AI services.
What We’re Looking For:
- 7-10 years of experience as a fullstack, backend, platform, or AI engineer.
- Hands-on experience building production AI, LLM, or agent-based applications.
- 3-5 years of Python backend engineering experience.
- 3 years of experience with Amazon Bedrock.
- 1-3 years of experience with Strands Agents / Strands SDK, LangGraph, LangChain, or comparable agent frameworks.
- Experience with Amazon Bedrock AgentCore or similar managed agent runtime/platform capabilities.
- Strong understanding of agentic application patterns, including tool calling, multi-turn workflows, session state, memory, prompt management, and evaluation.
- Experience designing and implementing REST APIs and backend service contracts.
- Experience with async processing, queues, retries, idempotency, and distributed job execution.
- Experience with AWS serverless services such as Lambda, API Gateway, SQS, DynamoDB, S3, IAM, KMS, CloudWatch, Step Functions, and Cognito.
- Experience with infrastructure-as-code, preferably AWS CDK in Python.
- Experience with CI/CD pipelines, automated testing, environment promotion, and rollback workflows.
- Experience building observable production systems using logs, metrics, traces, dashboards, and alarms.
- Ability to build reusable engineering patterns rather than one-off implementations.
- Strong understanding of security, least-privilege IAM, secrets management, tenant-aware access patterns, and secure API design.
- Strong communication skills and ability to document architecture, implementation decisions, operational patterns, and tradeoffs.
- Nice to have: experience with GitLab CI/CD, multi-tenant SaaS platforms, Cognito/OIDC, SOC 2-aligned controls, model evaluation workflows, or regulated enterprise environments.
Compensation Range: $150, 000 - $180, 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.
