Job Description

Job Title: Machine Learning Engineer

Location: Remote (Canada-based) – Occasional travel to Toronto or Ottawa may be required for emergent needs
Type: 7–8 Month Contract
Industry: Healthcare (Non-Profit)
Start Date: ASAP

About the Client:

We are assisting a respected non-profit healthcare organization in their search for a Machine Learning Engineer to develop a Proof of Concept (PoC) internal chatbot using state-of-the-art machine learning and NLP techniques. This role supports a broader initiative to enhance internal information access and automation.

Key Responsibilities:

• Architect and implement ML solutions with a focus on RAG (Retrieval-Augmented Generation), LLMs (Large Language Models), and robust NLP pipelines.
• Develop LLM-powered APIs and chat assistants using frameworks like LangChain, LlamaIndex, or equivalents.
• Utilize vector databases (e.g., pgvector, Pinecone, Weaviate) to manage and retrieve document embeddings.
• Ingest and preprocess structured/unstructured data from SharePoint, GitLab, Confluence, databases, wikis, and documents.
• Extend NLQ (Natural Language Query) capabilities and enhance prompt engineering.
• Fine-tune and train lightweight NLP models for task-specific performance.
• Manage and optimize ML pipelines deployed via Docker/Kubernetes, ensuring adherence to MLOps best practices.
• Integrate chatbot and ML services with platforms such as Microsoft Teams, VS Code, and RStudio.

Skills & Qualifications:

• Proven experience developing and deploying classical machine learning algorithms in production environments.
• Strong expertise in deep learning architectures, including RNNs, LSTMs, Transformers, GANs, and Graph Neural Networks.
• Advanced knowledge in Natural Language Processing (NLP) techniques such as Neural Machine Translation, Sentiment Analysis, Text Generation, Summarization, and Q&A systems.
• Skilled in working with vector databases (e.g., pgvector, Pinecone, Weaviate) for similarity search and document retrieval.
• Extensive experience in text data preparation, including cleaning, chunking, and embedding of textual content.
• Deep understanding of Retrieval-Augmented Generation (RAG) for content generation and NLP enhancement.
• Familiarity with advanced RAG techniques, including retrieval refinement and knowledge graph integration.
• Practical experience in LLM deployment, including infrastructure scaling and backend model serving.
• Knowledge of LLM inference optimization techniques such as quantization, pruning, and caching.
• Proficient in Python and SQL, with hands-on experience using frameworks like PyTorch, TensorFlow, LangChain, LlamaIndex, Haystack, FAISS, and Sentence Transformers.
• Capable of maintaining LLMs with performance monitoring, error detection, bias mitigation, and handling data drift.
• Experience designing and deploying RESTful APIs for ML model consumption.
• Proficient in containerization tools, especially Docker, and deploying via AWS ECS and ECR.

Additional Notes:

• Candidate must reside in Canada and be open to rare onsite visits (Toronto/Ottawa).
• Experience with secure, enterprise-grade deployment environments is a plus.
• Prior experience in healthcare, non-profit, or internal tooling projects is considered an asset.