{"id":4126199,"url":"https://alion.io/job/novara-software-engineer-ai-platforms","title":"Software Engineer-AI Platforms","company":{"id":679743,"name":"Novara","domain":"novara.com","url":"https://alion.io/company/novara","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":null},"role":"Backend","role_family":"Backend","seniority":"junior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Toronto, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"CI/CD","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"GraphQL","optional":false},{"name":"Incident Management","optional":false},{"name":"JavaScript","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Node JS","optional":false},{"name":"OpenAI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"RAG","optional":false},{"name":"React.js","optional":false},{"name":"Rest API","optional":false},{"name":"TypeScript","optional":false},{"name":"Amazon CloudWatch","optional":true},{"name":"Amazon ECS","optional":true},{"name":"Amazon S3","optional":true},{"name":"AWS Lambda","optional":true},{"name":"Datadog","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"LLM Guardrails","optional":true}],"status":"live","first_seen_at":"2026-11-11T21:18:37Z","employer_posted_date":"2026-11-11","last_verified_at":"2026-11-12T01:31:55Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Position Description\nThe Flex platform helps clients develop a comprehensive compliance program, leveraging technology to instill a culture of safety and maintain a productive workplace. The platform combines features tailored to the needs of our clients' businesses, including audits and inspections, incident management, flexible training, and reporting and insights.\nAs a member of our development team, the Software Engineer - AI Platform works across the full stack to deliver modern, AI-powered product functionality. You will build user-facing features in the Flex web application and the backend services, APIs, and LLM workflows behind them, so clients can keep their teams safe and manage risk.\nThis role suits an engineer with 2-3 years of professional experience who has shipped production features end to end and has hands-on exposure to LLM-based applications. You will own well-scoped features, work closely with senior engineers on architecture and AI integration patterns, and grow your depth in agentic workflows, MCP integrations, and evaluation practices.\nKnowledge, Experience, Requirements:\nRequired\n2-5 years of professional software engineering experience, including shipping production features across frontend and backend\nProficiency in Node.js and TypeScript/JavaScript\nExperience building responsive web UIs with a modern framework such as React, including state management and API integration\nAbility to build and consume well-typed REST or GraphQL APIs in Node.js and TypeScript, with sound handling of authentication, error handling, and data transformation\nHands-on experience building at least one application or feature using large language models via platforms such as AWS Bedrock, OpenAI, Anthropic, or similar\nWorking understanding of tools, function calling, and agents, and how they let LLM systems take action against existing APIs\nPractical experience with prompt engineering and iterating on prompts to improve output quality\nFamiliarity with Retrieval-Augmented Generation (RAG) concepts, embeddings, and vector-based search\nComfort working in an AI-first development environment, using AI tools for code generation, refactoring, test creation, and documentation, and reviewing their output critically\nExperience working with relational databases and writing efficient queries\nSolid grasp of data structures, algorithms, and fundamentals of system design\nGood debugging skills across the browser, server, and network\nExperience writing unit and integration tests and working with CI/CD pipelines\nClear communication and collaboration skills in an agile, team-based environment\nBachelor's degree in Computer Science, Engineering, or a related technical discipline preferred, or equivalent practical experience\nNice to have\nExposure to building or consuming Model Context Protocol (MCP) servers or similar AI integration layers\nExperience with LangChain, LangGraph, or similar orchestration frameworks\nFamiliarity with LLM evaluation approaches, guardrails, or output monitoring\nExperience with AWS services (e.g., Lambda, ECS, S3) or another major cloud provider\nExposure to observability tooling such as Datadog or CloudWatch\nInterest in or exposure to EHS, compliance, or safety software\nSuccess Criteria:\nDelivers well-scoped, production-ready AI-powered features end to end, from UI to backend service, with solid test coverage and minimal rework\nBuilds intuitive user experiences that present LLM outputs clearly, including loading states, error states, and ways for users to review or correct AI-generated results\nImplements LLM-driven workflows using services such as AWS Bedrock and LangChain, following patterns set by senior engineers\nContributes to MCP servers and tool integrations that expose existing APIs to AI systems, with appropriate authentication, input validation, logging, and failure handling\nApplies prompt engineering techniques to improve accuracy and consistency, and uses evaluation results to measure improvements\nImplements guardrails and monitoring for LLM outputs as defined by the team's standards\nValidates and refines AI-generated code before submitting it for review, and owns its quality\nBreaks down defined product requirements into clear tasks and estimates, and raises ambiguity early\nCommunicates progress, blockers, and tradeoffs clearly to engineering peers and product partners\nParticipates actively in code reviews, both giving and receiving feedback constructively\nShares useful AI workflows and learnings with the team through documentation and internal discussions\nSteadily grows ownership, moving from scoped features toward larger components and system-level design\nContributes positively to team culture by collaborating effectively and fostering continuous learning\nCompensation:\nAnnual Base Salary Range of:\nSoftware Engineer- 110k-130k CAD\nSenior Software Engineer- 130k-150k CAD\nAnnual Bonus Opportunity of 10%\nLocation:\nWe’re a remote-first team that gets together in our North York office in Toronto every Tuesday, and as needed for in-person team events. 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