Hybrid Senior Applied AI Scientist – Generative AI, Agentic Systems

at Messagepoint

• Job title: Senior Applied AI Scientist – Generative AI, Agentic Systems
• Job type: Full Time
• Experience level: Senior
• Salary: Not specified
• Degree requirement: Postgraduate Degree
• Tech skills: Apache, AWS, Azure, Cloud, Django, ElasticSearch, Google Cloud Platform, Microservices, MySQL, Postgres, Python, Ray, React, Spark, SQL
• Location requirements: Hybrid, Toronto, Canada

About the role

• Senior Applied AI Scientist at Messagepoint developing generative AI solutions using large language models. Focused on transforming research into production systems for customer communications.

Responsibilities

• Design and deploy production multi-agent AI systems using frameworks like LangGraph, OpenAI Assistants/Responses, Swarm, CrewAI, or AutoGen to solve complex business problems at scale
• Build production RAG pipelines implementing proven retrieval techniques including hybrid search, reranking, and knowledge graph integration for enterprise-grade performance
• Engineer and optimize prompts as versioned code using established techniques (Chain-of-Thought, ReAct, self-consistency) with rigorous testing and evaluation
• Implement comprehensive evaluation and monitoring pipelines with production KPIs, quality gates in CI/CD, and automated regression testing
• Architect scalable agentic workflows with persistent state management, error recovery, and production-grade reliability
• Deploy observability and debugging infrastructure using LangSmith, TruLens, DeepEval for production monitoring and issue resolution
• Integrate safety and compliance measures including PII detection, content filtering, guardrails, and security best practices
• Optimize system performance and costs through caching strategies, model routing, batch processing, and efficient resource utilization
• Build APIs and microservices for AI systems with proper versioning, documentation, and SLA management
• Champion AI-assisted development using tools like Claude Code, Augment, and Gemini CLI to maximize engineering productivity
• Stay current with relevant AI research - Read papers from top conferences (NeurIPS, ICML, ACL) to identify techniques applicable to our use cases
• Evaluate emerging techniques - Assess new methods for potential adoption, focusing on production viability and ROI
• Implement proven research concepts - Translate well-validated research into production when it provides clear value
• Conduct targeted experiments - Test specific hypotheses about system improvements with data-driven validation

Requirements

• 5+ years of software engineering experience with at least 2 years shipping LLM-powered products to production
• MS in Computer Science or equivalent experience (PhD is a plus but not required) with strong foundation in deep learning, NLP, or machine learning
• Solid understanding of transformer architectures, attention mechanisms, and modern deep learning techniques
• Ability to read and understand research papers to stay current with the field and identify useful techniques
• Proven track record shipping LLM applications with real users, SLAs, and production monitoring
• Production deployment of multi-agent systems using frameworks like LangGraph, OpenAI Assistants, or similar
• Experience building RAG pipelines at scale with vector databases, retrieval optimization, and quality metrics
• Expert-level prompt engineering with systematic testing and optimization approaches
• Building APIs and microservices for AI systems with proper error handling and scalability
• Distributed computing and storage experience (e.g., Apache Spark, Ray, cloud storage solutions)
• Strong software engineering practices including code review, testing, CI/CD, and documentation
• Active use of AI-assisted development tools like Claude Code, Augment, Gemini CLI to maximize productivity
• Experience with production ML/AI infrastructure including monitoring, debugging, and performance optimization
• Python expertise with async programming and modern web frameworks (FastAPI, Django)
• Vector database experience (pgvector, Pinecone, Weaviate, Milvus, or FAISS)
• LLMOps/AgentOps tools familiarity (LangSmith, TruLens, DeepEval, Giskard)
• SQL and search systems experience (PostgreSQL, MySQL, Elasticsearch, Solr, OpenSearch)
• Cloud platform proficiency (AWS, GCP, or Azure) with containerization and orchestration
• API design and microservices architecture for AI systems
• Version control and collaborative development practices with Git

Benefits

• Access to latest models, robust compute infrastructure, and top-tier development tools
• Support for attending major AI conferences to stay current with the field
• Focus on sustainable engineering practices and healthy team dynamics