Description:

Torinit Technologies Inc. is a Canadian-based digital consulting company.

At Torinit, we don't just serve our clients; we work with them to create transformative digital journeys by leveraging the latest technologies and world-class best practices.

Our goal is to create an environment of continuous learning and success for our team and our clients.

We are a fast-growing team of passionate problem-solvers and life-long learners who aren't afraid of tackling complex problems.

We've got a talented team of skilled, friendly, and driven individuals spanning the globe (primarily based in Canada and India), now we're looking to add more world-class talent to our team.

Role Overview:

We are seeking a Machine Learning Engineer with 35 years of experience to design, build, deploy, and optimize ML/AI solutions across a range of business problems.

The ideal candidate understands the full ML lifecyclefrom data preprocessing and experimentation to deployment, monitoring, and continuous improvement.

The role requires strong engineering fundamentals, modeling expertise, and practical knowledge of MLOps, along with the ability to convert business use cases into scalable ML pipelines.

Key Responsibilities:

Model Development & Experimentation:

- Build, train, and evaluate machine learning models (classification, regression, clustering, NLP, deep learning).

- Develop custom models or adapt pre-trained models (transfer learning).

- Conduct feature engineering, feature selection, and data transformation.

- Perform hyperparameter tuning using tools like Optuna, Ray Tune, or built-in frameworks.

- Evaluate model performance using appropriate metrics (AUC, F1, MAE, BLEU, WER, etc.

Data Engineering & Processing:

- Work with structured and unstructured data (text, images, audio, logs, documents).

- Build data pipelines using Python, SQL, Spark, Airflow, or similar.

- Apply data quality checks, deduplication, anomaly detection, and data validation (Great Expectations, pydantic).

ML Deployment & MLOps:

- Deploy models as APIs using FastAPI, Flask, Node, or cloud-native tools.

- Implement CI/CD pipelines for ML (GitHub Actions, GitLab, Jenkins).

- Use containerization & orchestration (Docker, Kubernetes).

- Manage models using MLflow, Vertex AI, SageMaker, Azure ML, or Databricks.

- Implement automated retraining, model versioning, rollback, and monitoring.

- Handle inference optimizations (quantization, batching, caching).

LLM & GenAI (Preferred but not mandatory):

- Work with foundation models like GPT, LLaMA, Claude, Mistral, etc.

- Build RAG pipelines with vector databases (Qdrant, Pinecone, FAISS, Elasticsearch).

- Prompt engineering and evaluation.

- Optimize embeddings, context windows, and chunking strategies.

- Integrate LLMs into production APIs and workflows.

Cloud & Infrastructure:

- Experience with at least one cloud provider (AWS, Azure, GCP).

- Use managed ML services like:

AWS: SageMaker, Lambda, Textract, Comprehend.

Azure: AI Foundry, Functions, Search.

GCP: Vertex AI, Cloud Run.

- Manage GPU/CPU compute workloads efficiently.

Software Engineering Best Practices:

- Write clean, modular, well-tested Python code.

- Maintain repositories adhering to branching, versioning, and code review standards.

- Use design patterns for scalable ML pipelines.

- Build internal tools for automation, testing, and monitoring.

Cross-functional Collaboration:

- Work closely with product managers, data engineers, backend engineers, and business analysts.

- Translate business problems into ML solutions with measurable ROI.

- Document technical designs, APIs, models, architecture diagrams.

Monitoring, Observability & Optimization:

- Implement model monitoring for data drift, concept drift, performance degradation.

- Evaluate model reliability, bias, fairness.

- Implement logging & metrics (Prometheus, Grafana, ELK).

- Continuously improve model performance and resource usage.

Required Skills & Experience :

Technical Skills :

- Strong Python skills (NumPy, Pandas, Scikit-learn, PyTorch or TensorFlow).

- Proficiency in ML algorithms, data structures, and ML system design.

- Experience with deep learning, especially CNN/RNN/Transformers.

- Knowledge of LLMs, embeddings, tokenizer concepts (preferred).

- Strong understanding of vector databases (Qdrant, FAISS, Pinecone).

- Hands-on experience with REST APIs, microservices, and cloud-native deployment.

- Familiarity with SQL, NoSQL, and data warehouses (Snowflake/BigQuery/Redshift).

MLOps & Infrastructure :

- Docker, Kubernetes, Terraform (good to have).

- MLflow or equivalent for experiment tracking.

- CI/CD automation experience.

- GPU optimization and parallel processing experience.

Soft Skills :

- Strong problem-solving abilities.

- Excellent communication for explaining technical concepts to non-tech stakeholders.

- Ownership mindset and ability to work independently.

- Ability to work in a fast-paced, iterative, PoC-driven environment.

- Curiosity to continuously learn new technologies.

Preferred Qualifications :

- Bachelors or Masters degree in Computer Science, Engineering, Mathematics, or equivalent experience.

- Experience building ML products end-to-end.

- Experience in domains like FinTech, Healthcare, Retail, Manufacturing, or Real Estate is a plus.

- Contributions to open-source ML frameworks are a bonus.