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.

We’re an equal opportunity employer committed to increasing diversity and inclusion in today’s workforce. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. Minorities, women, LGBTQ candidates, and individuals with disabilities are encouraged to apply. If you require an accommodation, please review our accessibility policy and reach out to our accessibility officer with any questions.