Senior Applied AI Engineer

Knowledge, Experience, Requirements:

• SaaS Product Experience: 5+ years of software development experience, with at least 2 years spent

building and scaling production-grade AI features in a cloud-native SaaS environment.

• Educational Background: Strong academic background in Computer Science, Data Science, Software

Engineering, or a highly quantitative field (e.g., Mathematics, Physics, Statistics). Bachelor's degree in

Computer Science, Engineering, or a related technical discipline preferred.

• Technical Stack: You must have hands-on, production experience with the following technologies:

- Languages: Python (Expert/Senior level), TypeScript/JavaScript (Strongly Preferred).

- AI Frameworks: LangGraph, LangChain, Vercel AI SDK or equivalent.

- AWS Infrastructure: Amazon Bedrock, ECS Fargate, S3, SQS, EventBridge, KMS, AWS Lambda, Amazon

Comprehend, IAM.

- Databases & Search: PostgreSQL / pgVector, Amazon OpenSearch Serverless, SQLAlchemy.

- Data Processing: Pandas, NumPy, PyPDF, Layout-OCR engines.

- API & Protocols: REST, Server-Sent Events (SSE), Webhooks, and Model Context Protocol (MCP).

• Hands-on AWS Background: Strong experience designing secure AWS architectures using Least Privilege IAM execution roles, SigV4 API signing, and KMS envelope encryption.

• RAG at Scale: Experience indexing and searching datasets scaling into millions of document chunks, with a proven understanding of Direct Bulk Indexing APIs.

• System and Security Architecture: Solid understanding of authentication patterns (OAuth 2.0, JWT passthrough) and how to isolate data logically in multi-tenant shared databases.

• Clean Code Advocate: Demonstrated ability to write clean, unit-tested, and well-documented Python

code, utilizing self-correction loops and graceful degradation patterns to handle model latency and API

rate-limiting limits.

• Collaboration & Agile: Strong communication and collaboration skills, thriving in an agile, team-based environment.

Nice-to-Haves: The following experience will be highly valued:

• Machine Learning & Predictive Modeling: Practical experience training and serving classical ML models (e.g., Isolation Forest, One-Class SVM, or unsupervised clustering) for behavioral baselining, anomaly detection, or predictive risk scoring.

• Experience developing React-based micro-frontends or canvas-style Generative UI layouts.

• Contributions to the open-source Model Context Protocol (MCP) ecosystem.

• Background in EHS (Environmental Health & Safety) or ESG (Environmental, Social, and Governance)

software systems.

• AWS Certified Machine Learning – Specialty or AWS Certified Solutions Architect – Professional.

Success Criteria:

• Core AI & Orchestration - Key expectations for AI platform engineering:

• Agentic State Machines: Design and implement complex, multi-agent state machines and stateful

graphs using LangGraph and LangChain to support autonomous decision-making and self-correcting

loops.

• Dynamic Agent & Workflow Registries: Architect database-driven registries (using PostgreSQL) to

dynamically discover, load, and configure agent definitions, system prompts, and task workflows at

runtime without redeploying code.

• Optimized LLM Routing: Build intent-based routing engines that evaluate user queries and direct them to either deterministic execution layers (e.g., Python code interpreters running over in-memory DataFrames) or semantic retrieval layers (RAG).

• Observability & Cost Tracking: Configure centralized telemetry pipelines and AI Gateways for token tracking, caching, rate limiting, and real-time streaming of internal graph execution traces (via ServerSent Events).

• Advanced RAG & Data Engineering - Key expectations for data pipelines and search systems:

- Production-Grade RAG on AWS: Build and maintain a dual-engine vector search architecture: Amazon OpenSearch Serverless for unstructured policy, regulation, and SOP document retrieval, and PostgreSQL + pgvector for structured transactional logs, incident histories, and audit records.

- Serverless Ingestion Pipelines: Build scalable, event-driven ingestion pipelines using AWS S3, SQS, EventBridge, and AWS Fargate to parse raw documents (PDF, Word, CSV) into Markdown.

- Context Preservation & Visual RAG: Implement advanced chunking strategies, including slidingwindow paragraph overlaps, header breadcrumb perpetuation, and Vision Transformer (ViT) visualenrichment models to summarize embedded charts, diagrams, and stamps.

- Automated PII Redaction: Integrate Amazon Comprehend or custom LLM classifiers inside the Fargate worker container to scrub names, emails, and SSNs before data is indexed.

• EHS Integration & MCP - Key expectations for security, integration, and guardrails:

- Model Context Protocol (MCP) Servers: Build standardized, decoupled MCP servers that wrap legacy REST APIs (Java/C# backends), exposing databases, schemas, and actions as dynamically discoverable tools for the AI agents.

- Prompt-Independent Security (RBAC): Implement user-delegated token pass-through (JWT forwarding) so that data-access permissions are enforced mechanically by the legacy API. Design hard metadata filters (where tenant_id = jwt.tenant_id) in OpenSearch and pgvector to ensure multi-tenant isolation.

- HITL Write Guardrails: Configure Human-in-the-Loop (HITL) state breakpoints in LangGraph to halt write-mutations, broadcasting the pending action to administrators for UI-based approval.

• Generative UI Layouts - Key expectations for UI integration:

- Dynamic Component Rendering: Design structured JSON widget schemas (representing tables, Recharts graphs, checklists, and forms) generated dynamically by the backend agents to enable zerostate rendering of layouts in the Next.js UI.

Compensation:

Annual Base Salary Range of CA$144k-165k
Annual Bonus Opportunity of 10%