Key Responsibilities

- Design, develop, train, evaluate, and deploy machine learning and deep learning models.
- Collect, clean, transform, and analyze structured and unstructured datasets.
- Perform feature engineering, data preprocessing, model selection, and hyperparameter tuning.
- Develop supervised, unsupervised, and semi-supervised machine learning solutions.
- Work with algorithms for classification, regression, clustering, recommendation, and anomaly detection.
- Develop and integrate Generative AI, Large Language Models (LLMs), NLP, and computer vision solutions where applicable.
- Build AI/ML pipelines for model training, validation, deployment, and monitoring.
- Implement MLOps practices for model versioning, CI/CD, deployment, monitoring, and retraining.
- Deploy models using cloud platforms, APIs, containers, and scalable infrastructure.
- Optimize models for performance, scalability, accuracy, latency, and resource utilization.
- Conduct experiments and analyze model performance using appropriate evaluation metrics.
- Collaborate with data engineers, software developers, data scientists, product teams, and business stakeholders.
- Translate business requirements into practical AI/ML solutions.
- Maintain technical documentation for models, datasets, experiments, APIs, and deployment processes.
- Monitor production models for performance degradation, data drift, and model drift.
- Stay current with developments in AI, ML, deep learning, Generative AI, and emerging technologies.

Required Technical Skills

- Strong programming skills in Python.
- Good understanding of Machine Learning and Deep Learning concepts.
- Hands-on experience with frameworks such as:
- Scikit-learn
- PyTorch
- TensorFlow/Keras
- Strong knowledge of NumPy, Pandas, Matplotlib/Seaborn, and related data-processing libraries.
- Understanding of statistics, probability,



linear algebra, and optimization.
- Experience with SQL and working with relational databases.
- Knowledge of data preprocessing, feature engineering, model evaluation, and validation techniques.
- Familiarity with REST APIs and software development practices.
- Knowledge of Git, Docker, and CI/CD.
- Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud is preferred.

Generative AI / LLM Skills

- Understanding of Generative AI and Large Language Models (LLMs).
- Experience with LLM APIs and/or open-source models.
- Knowledge of prompt engineering and LLM evaluation.
- Experience building RAG (Retrieval-Augmented Generation) applications is preferred.
- Familiarity with vector databases and embeddings.
- Exposure to frameworks such as LangChain, LlamaIndex, or similar is an advantage.
- Understanding of model fine-tuning, LoRA/PEFT, and model optimization is a plus.
- Awareness of responsible AI, security, privacy, and AI governance principles.

MLOps & Cloud Skills

- Experience deploying ML models into production environments.
- Knowledge of Docker and Kubernetes is an advantage.
- Familiarity with ML platforms such as MLflow, Kubeflow, SageMaker, Azure ML, or Vertex AI.
- Understanding of CI/CD pipelines and automated model deployment.
- Experience with cloud storage, compute, databases, and monitoring services.
- Knowledge of model monitoring, data drift, model drift, and performance monitoring.

Soft Skills

- Strong analytical and problem-solving skills.
- Excellent programming and debugging abilities.
- Ability to convert business problems into scalable AI/ML solutions.
- Strong communication and collaboration skills.
- Ability to work independently as well as within cross-functional teams.
- Robust curiosity and willingness to learn emerging AI technologies.
- Good documentation and presentation skills.

📌 AI/ML Engineer - repentigny
🏢 Spait Infotech
📍 Repentigny