Applied AI Engineer

Ampliwork
Montreal
Workplace: HybridFull timeFunction: Data Science & Machine LearningExperience: 2-5 yearsSkills: ["Problem-solving","Adaptability","Continuous learning","Collaboration"]

Design, build, and scale enterprise-grade, cloud-native AI systems for workflow automation in regulated industries. Develop robust agentic workflows using modern frameworks, implement deep learning and machine learning solutions, and build NLP and generative AI capabilities including RAG pipelines with transformer models, embeddings, and prompt engineering. Optimize RAG with chunking/vector search strategies, apply MLOps observability and evaluation, and collaborate to deliver reliable, scalable products.

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FursaFursa
Ampliwork
Ampliwork
1 day ago

Applied AI Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 10 hours agoStatus: Live
Reposted: similar role first listed 6 months ago

Job Summary

Design, build, and scale enterprise-grade, cloud-native AI systems for workflow automation in regulated industries. Develop robust agentic workflows using modern frameworks, implement deep learning and machine learning solutions, and build NLP and generative AI capabilities including RAG pipelines with transformer models, embeddings, and prompt engineering. Optimize RAG with chunking/vector search strategies, apply MLOps observability and evaluation, and collaborate to deliver reliable, scalable products.
Location: Montreal
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Lead design and development of robust, scalable, cloud-native software architectures.
  • •Implement complex features using Python and modern development frameworks and best practices.
  • •Develop sophisticated agentic workflows leveraging industry frameworks.
  • •Build NLP and generative AI solutions, including transformer models and RAG pipelines (prompting, embeddings, chunking, vector search).
  • •Optimize RAG pipelines and ensure system observability and monitoring using MLOps practices and evaluation methodologies.

Key Requirements

  • •2–5 years of hands-on experience building and deploying enterprise-level, cloud-native, scalable systems.
  • •Advanced proficiency in Python and its development ecosystem.
  • •Experience developing agentic workflows using industry-leading frameworks.
  • •Strong understanding of deep learning and machine learning fundamentals plus NLP fundamentals.
  • •In-depth knowledge of generative AI fundamentals, especially RAG pipelines (transformers, embeddings, tokenization, prompt engineering/tuning, chunking, vector databases, similarity search) and MLOps/observability/evaluation.
Experience:2-5 yearsEnterpriseCloud-nativeMachine learningGenerative AIRAG
Skills:Problem-solvingAdaptabilityContinuous learningCollaboration
Languages:English
Tech Stack:PythonJavaScriptBERTTransformersNLPDeep LearningMachine LearningRAGRetrieval Augmented GenerationEmbeddingsTokenizationPrompt engineeringPrompt tuningVector databasesSimilarity searchMLOpsNatural Language ProcessingText-to-SQLMCPModel Context Protocol

Company Brief

Ampliwork
Connects companies with vetted remote software engineers and product teams, specializing in scaling technology teams through nearshore talent, staff augmentation, and managed engineering services to accelerate product delivery.
Industry: Recruitment Agencies
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