- Co-create solutions with business and technical stakeholders through workshops, rapid iterations, and hands-on delivery

- Locate, qualify, and secure access to data required for each use case, working directly with the Data Engineer

- Translate use cases into production-ready GenAI and agentic AI solutions, including RAG architectures, intelligent assistants, and AI-enabled workflows

- Prototype, test, deploy, monitor, and improve solutions in real client environments using feedback from users and domain experts

- Collaborate with Data Engineers, Software Engineers, Foundations Architects, Governance experts, Business Value Advisors, and Service Delivery Managers

- Balance speed, quality, cost, security, and maintainability while making technical and delivery trade-offs

- Define success criteria including adoption, performance, reliability, risk, cost, and measurable business value

- Ensure solutions are documented, governed, and transferable so clients can operate them with confidence

- Turn successful delivery into reusable patterns, accelerators, and building blocks for future engagements

Requirements

- Typically 5-10 years of relevant experience in AI Engineering, Machine Learning, Software Engineering, Data Engineering, or technical consulting

- Hands-on experience delivering AI, GenAI, or software solutions into production

- Experience working directly with clients or in complex stakeholder environments

- Evidence of turning complex use cases into adopted measurable solutions
- A degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field, or equivalent practical experience





- Strong Python development skills

- API integration experience and solid software-engineering practices

- Hands-on experience with LLMs and GenAI, including RAG, embeddings, vector search, AI agents, and agentic workflows

- Familiarity with AI frameworks such as LangChain, LlamaIndex, LangGraph, Semantic Kernel, AutoGen, or similar

- Experience integrating and deploying AI solutions within enterprise environments and on Azure, AWS, or GCP

- Working knowledge of Docker, Git, CI/CD, MLOps/LLMOps, monitoring, security, data privacy, governance, and responsible AI principles

Core Competencies

Demonstrates expertise in delivering AI and GenAI solutions, with solid capabilities in Python development, API integration, and enterprise deployment on platforms like Azure, AWS, or GCP. Proven ability to collaborate with diverse stakeholders and translate complex use cases into measurable, production-ready solutions.

Highest-signal resume keywords

- AI Engineering

- GenAI Solutions Delivery

- Python Development

- API Integration

- Enterprise Deployment

ATS Optimization Keywords

Hard Skills

- AI Engineering

- Machine Learning

- Software Engineering

- Data Engineering

- Python Development

- API Integration

- LLMs

- GenAI

- RAG Architectures

- MLOps

Soft Skills

- Collaboration

- Stakeholder Engagement

- Problem Solving

- Adaptability

- Communication

Industry Keywords

- Data Privacy

- Governance

- Responsible AI

- AI Frameworks

- Enterprise Environments

Tools & Technologies

- Azure

- AWS

- GCP

- Docker

- Git

- CI/CD

- LangChain

- LlamaIndex

- LangGraph

- Semantic Kernel

📌 Forward Deployed AI Engineer (Montreal)
🏢 Jobtailor
📍 Montreal