Applied AI Engineer

Ampliwork•Montreal, QC

1d•Hybrid

About The Position

As an Applied AI Engineer, you will be instrumental in designing, building, and scaling our enterprise-level systems. Your expertise will be crucial in developing cutting-edge solutions that leverage the latest in AI and machine learning.

Requirements

Junior & Mid-level experience as a hands-on software engineer, with a proven track record of building and deploying enterprise-level, cloud-native, and scalable systems.
Advanced proficiency in Python and its associated development ecosystem.
Demonstrable experience developing Agentic Workflows using industry-leading frameworks.
Solid understanding of Deep Learning and Machine Learning fundamentals.
Strong grasp of Natural Language Processing (NLP) fundamentals.
In-depth knowledge of Generative AI fundamentals, particularly RAG pipelines and related technologies (e.g., transformer models, embeddings, tokenization, prompt engineering/tuning, chunking strategies, vector databases, similarity searches).
Experience with RAG pipeline optimization techniques and familiar with MLOps, observability, and monitoring tools.
Proficiency in using evaluation metrics for AI models.
Exceptional problem-solving skills and the ability to adapt to evolving project requirements.
A positive attitude, a drive for continuous learning, and a desire to grow with a disruptive company.
Ability to effectively manage and navigate multiple concurrent projects and priorities.

Nice To Haves

Experience developing multi-agent systems and implementing Agentic reasoning design patterns like ReAct, Plan and Solve Prompting, or Program Aided Reasoning (PAR).
Demonstrated work on Text-to-SQL, function/tool calling, and integrations with external third-party tools.
Experience with Small Language Model (SLM) fine-tuning on domain-specific data and embedding model tuning.
Understanding of agentic protocols like Model Context Protocol (MCP) and A2A.
Experience with web development using latest JavaScript frameworks for building robust user interfaces.

Responsibilities

Lead the design and development of robust, scalable, and cloud-native software architectures.
Implement complex features and functionalities using Python and modern development frameworks and best practices.
Develop sophisticated Agentic Workflows leveraging leading industry frameworks.
Apply your deep understanding of Deep Learning concepts (e.g., Stochastic Gradient Descent, Backpropagation) and Machine Learning fundamentals (e.g., clustering, classification, regression, tree-based algorithms) to solve real-world problems.
Design scalable user interface using latest JavaScript frameworks.
Leverage your knowledge of Natural Language Processing (NLP) fundamentals (e.g., BERT, Bag-of-Words, Part-of-speech tagging, entity recognition, sentiment analysis) to extract insights from diverse datasets.
Drive the implementation of Generative AI solutions, demonstrating expertise in Transformer models, embeddings, tokenization, prompt engineering, prompt tuning, and a thorough understanding of Retrieval Augmented Generation (RAG) pipelines.
Strategize and implement various data chunking strategies and work with diverse vector database technologies and their underlying similarity search techniques.
Optimize RAG pipelines and ensure system observability and monitoring using MLOps practices and relevant tools.
Utilize and evaluate solutions with various AI model evaluation methodologies and tools.
Navigate and prioritize multiple complex requirements in a fast-moving environment.
Contribute to a positive and collaborative team culture, driving innovation and continuous improvement.