Lead Software Engineer
- ON, Canada
- Ontario, Canada, Canada
$170,000 to $190,000 CAD / year · Full-time
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Job details
- Pay
- $170,000 to $190,000 CAD / year
- Job type
- Full-time
- Apply by
- Experience
- 8+ years of software engineering, with hands-on commercial experience in IR, NLP, LLM and agentic systems running in production
- Posting language
- English
- Posted
Original job description
Lead Software Engineer – Agentic AI, NLP & Production ML | Applied AI Lab
I’m recruiting for a global leader in professional information and enterprise software. Their applied AI lab needs a Lead Engineer who has built agentic and LLM systems that run in production, not just in a notebook.
The opportunity
This is a lab that experiments, builds and delivers. You’ll lead technical direction across multiple initiatives, set engineering standards that scale, and work with research scientists to turn ideas into products used by legal, tax, accounting and government professionals worldwide.
What you’ll do
- Provide technical leadership: set the standards and best practices that help a global team move faster
- Own the full lifecycle, building, testing and shipping high-quality ML-powered solutions
- Build large-scale data pipelines that help researchers train novel ML algorithms, and high-performing systems for online delivery
- Design and optimise multi-agent systems, including tool calling, context management and workflow orchestration
- Partner with research scientists to evaluate, prototype and productionise new concepts
- Lead engagement across Product, UX, Tech and Business, taking ambiguous problems all the way to a system customers can trust
- Mentor engineers and raise the technical bar across the team
What you’ll bring
- 8+ years of software engineering, with hands-on commercial experience in IR, NLP, LLM and agentic systems running in production
- Deep expertise in agentic AI: tool use, multi-agent orchestration, context management, and observability/telemetry (tools like Langfuse or Braintrust will be on their radar)
- Expert production Python: clean, testable, maintainable code, ideally with Pydantic/PydanticAI, plus familiarity with PyTorch and Hugging Face
- Comprehensive evaluation experience: end-to-end quality measurement of agents in live systems, not just offline metrics
- Strong NLP grounding: NER, information extraction, information retrieval
- Experience with MLOps/ModelOps, automation, monitoring and cloud-native apps on AWS, Azure or similar
- Understanding of the maths behind ML, including probabilistic models
- Proven ability to lead stakeholders across Product, UX, Tech and Business
- A track record of mentoring and technical leadership
- Degree in Computer Science or a related field
More about this job
Responsibilities
Lead technical direction and engineering standards across applied AI initiatives, building and shipping production ML solutions, large-scale data pipelines, and multi-agent systems. Partner with research scientists and cross-functional stakeholders to turn concepts into trusted customer products, while mentoring engineers and raising the team's technical bar.
Requirements
Requires 8+ years of software engineering experience and hands-on commercial experience with production information retrieval, NLP, LLM, and agentic systems. Candidates should bring expert Python skills, agent observability and evaluation experience, MLOps and cloud knowledge, strong NLP and ML foundations, stakeholder leadership, mentoring experience, and a degree in Computer Science or a related field.
Skills
- Agentic AI
- Large Language Models
- Natural Language Processing
- Information Retrieval
- Named Entity Recognition
- Information Extraction
- Production Python
- Multi-Agent Orchestration
- Tool Calling
- Context Management
- Model Evaluation
- MLOps
- ModelOps
- AWS
- Azure
- Technical Leadership
Visa sponsorship
Not detected in the job text
Categories
- Software
- Technology
- Engineering
- Data & Analytics
- Management & Leadership
Keywords
- Agentic AI
- Large Language Models
- Natural Language Processing
- Information Retrieval
- Named Entity Recognition
- Information Extraction
- Python
- Pydantic
- PydanticAI
- PyTorch
- Hugging Face
- Langfuse
- Braintrust
- Multi-Agent Systems
- Tool Calling
- Context Management
- Workflow Orchestration
- Model Evaluation
- Observability
- Telemetry
- MLOps
- ModelOps
- AWS
- Azure
- Cloud-Native Applications
- Data Pipelines
- Probabilistic Models
- Production Machine Learning
- Applied AI
- Technical Leadership