AI Engineer

You deserve an awesome career! At Execulink, you'll join a team of talented individuals who feel like family and support your growth and career goals. As we rapidly grow, we continue our unwavering commitment to live and breathe our Vision, Mission and Values. While enhancing life at home, work and play, we continue to grow our team with innovative individuals who are as excited about creating an awesome experience for our customers as we are! 

About this Opportunity:

Are you an AI enthusiast ready to make a real impact? Execulink Telecom is seeking a talented AI Engineer to join our team in Woodstock, Ontario. In this exciting role, you’ll design and build advanced AI systems—think RAG pipelines, LLM-powered applications, and agentic workflows—that transform business challenges into smart solutions.

You’ll collaborate across teams, leverage the latest AI frameworks, and deploy scalable, production-ready models that drive measurable results. If you’re passionate about innovation, love tackling complex problems, and want to be part of a forward-thinking company, this is the opportunity for you.

Join us and be a key player in shaping the future of AI-powered telecom!

Position Details:

• Woodstock, ON
• Permanent, Full Time
• Hours of Work
• Monday to Friday 8:30am – 5:00pm
• Hybrid role opportunity
• Required to work in office a minimum of 3 days per week
• Ability to work flexible hours outside of regular office hours may be required for maintenance activities

Responsibilities:

• Design, implement, and optimize Retrieval-Augmented Generation (RAG) pipelines that ground LLM responses in enterprise knowledge
• Develop and maintain agentic workflows and orchestration systems for multi-step reasoning and tool use
• Build and deploy machine learning models that solve real-world business challenges including predictive and classification problems
• Integrate LLMs and AI agents with APIs, databases, and enterprise applications
• Prepare and transform datasets for model training, testing, and validation
• Implement monitoring, evaluation, and guardrails for production AI systems
• Collaborate with data engineering and infrastructure teams to ensure scalability and performance optimization
• Research and evaluate emerging AI frameworks, vector stores, and tools to continuously improve system capabilities
• Contribute to best practices in AI security, data privacy, and prompt engineering

HR Responsibilities:

• None

Qualifications:

• 3-6 years of software engineering experience, ideally in backend, ML, or data-intensive systems
• Bachelor's degree in Computer Science, Data Science, Engineering, or related field
• Strong programming skills in Python and experience with ML frameworks (TensorFlow, PyTorch, scikit-learn, or similar)
• Hands-on experience with LLM application development including RAG pipelines, embeddings, and vector databases
• Proficiency in SQL and understanding of data preprocessing, model validation, and performance metrics
• Experience with cloud platforms (Azure preferred) and hybrid cloud/on-premises infrastructure
• Understanding of containerization (Docker/Kubernetes) and microservices architecture
• Knowledge of prompt engineering, model evaluation, and production AI best practices
• Experience in PostgreSQL and PGVector or similar Vector / Relational DBs
• Experience with structured data vector parsing, DevOps CI/CD Pipelines
• Experience with agent orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, DSPy, or similar) is preferred
• Familiarity with vector database implementations (pgvector preferred) is considered an asset
• Knowledge of MLOps tools, automated pipelines, and AI observability platforms is preferred
• Experience with MCP (Model Context Protocol) or multi-agent systems is considered an asset
• Background in information retrieval, search ranking, or knowledge graphs is preferred

Required Skills & Abilities:

• Strong problem-solving skills with ability to design solutions for complex technical challenges
• Excellent coding practices with focus on performance, scalability, and maintainability
• Ability to translate business requirements into technical implementations
• Strong communication skills for technical documentation and stakeholder updates
• Collaborative mindset with ability to work across data, engineering, and business teams
• Continuous learner staying current with emerging AI technologies and industry trends