Associate Vice President driving AI operations and observability strategies at TD Bank. Leading transformation for enterprise technology operations to enhance resilience and incident intelligence.
About the role
- AI/ML Architect leading design, development, and operationalization of AI solutions for ELEKS. Collaborating with cross-functional teams to translate requirements into robust AI systems.
Responsibilities
- Design end-to-end AI/ML architectures, including data ingestion pipelines, feature stores, model training, deployment patterns, and monitoring frameworks
- Lead the evaluation and selection of AI/ML tools, frameworks, cloud components, and platforms
- Define standards, best practices, and governance frameworks for responsible AI usage
- Partner with product and engineering leadership to shape the long-term AI roadmap
- Provide expert guidance across the full ML lifecycle: data preparation, modeling, experimentation, optimization, deployment, monitoring
- Architect scalable solutions using Python-based ML stacks (e.g., TensorFlow, PyTorch, Scikit-Learn) and modern cloud environments (AWS, Azure, GCP).
- Support the development of LLM-based applications, vector database architectures, and retrieval-augmented generation (RAG) systems
- Evaluate new AI capabilities (e.g., agent frameworks, fine-tuning strategies, MLOps automation)
- Oversee the technical design of AI projects and ensure solution quality, reliability, and security
- Work with cross-functional teams to define clear success metrics for AI initiatives
- Conduct architecture reviews, code reviews, and technical deep dives
- Mentor engineers and data scientists to elevate technical excellence.
Requirements
- 7+ years of experience in data science, machine learning, or AI engineering
- 3+ years in a senior or principal-level architectural role
- Strong proficiency in Python and common ML/AI frameworks (TensorFlow, PyTorch, Scikit-Learn, transformers libraries)
- Hands-on experience with:
- Cloud AI services (AWS Sagemaker, Azure ML, GCP Vertex AI)
- Data engineering tools (Spark, Databricks, Airflow, Kafka)
- LLM architectures, fine-tuning, embeddings, vector stores (FAISS, Pinecone, Weaviate)
- MLOps tools (MLflow, Kubeflow, DVC, CI/CD pipelines)
- Solid understanding of distributed computing, APIs, microservices, and containerization (Docker, Kubernetes).
Benefits
- Close cooperation with a customer
- Challenging tasks
- Competence development
- Team of professionals
- Dynamic environment with a low level of bureaucracy
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