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GenAI Developer
LinkedIn Recruiter Post
TCS Canada is hiring recent graduates for GenAI Developer roles, focusing on Generative AI, LLMs, RAG, and AI Agents. Candidates will work on real - world AI solutions in an Agile environment.
About the role
- Staff Developer AI/ML at Benevity, enhancing technology for social action with robust ML architecture. Leading end-to-end development, mentoring engineers, and aligning AI strategy with industry best practices.
Responsibilities
- Design and oversee the development of robust end-to-end ML architecture, from data ingestion and feature stores to model serving and monitoring.
- Define the long-term roadmap strategy for our AI infrastructure and orchestration framework.
- Oversee the design, implementation, and maintenance of our AI/ML ecosystem.
- Set the standard for MLOps - you will ensure that our ML ecosystem is as testable, maintainable, and scalable as our core application code.
- Cross-functional leadership - by working closely with Product Managers, Data Scientists and ML Engineers to translate business problems into concrete technical requirements.
- Act as a force multiplier for the team by conducting high-level design reviews and mentoring engineers on system design and performance optimization.
- Architect and evolve LLM-powered applications (e.g., copilots, search, assistants, agents), including RAG pipelines, tool integrations, and multi-step reasoning workflows.
- Design and implement robust evaluation frameworks for GenAI systems, incorporating offline benchmarks, online metrics, and human-in-the-loop feedback.
- Drive best practices for prompt engineering, agent design, and orchestration frameworks, ensuring maintainability and performance at scale.
- Establish guardrails and safety mechanisms for GenAI applications, including prompt injection defenses, hallucination mitigation, and responsible AI practices.
- Establish the golden path for model versioning, A/B testing, and automated rollbacks for identifying and mitigating drifts.
- Ensure AI architectural strategy aligns with industry best practices and standards, complies with security policies and industry regulations.
- Identify opportunities for process improvements and implement solutions to enhance platform performance and efficiency.
Requirements
- Bachelor's or Master's degree in Computer Science, Mathematics, or a related field, or equivalent deep professional experience.
- 7+ years of software engineering experience, with at least 4+ years architecting and deploying ML models in production at scale.
- Proven experience operating at a Staff or Senior level, including technical leadership, architecture ownership, and mentoring engineers.
- Deep expertise in MLOps and production ML systems, including model training, evaluation, deployment, monitoring, and lifecycle management.
- Strong experience with cloud platforms (AWS, Azure, or Google Cloud), including designing and operating scalable, distributed AI/ML workloads.
- Solid understanding of data architecture and data engineering, including data pipelines, feature engineering, data modeling, and large-scale data processing.
- Experience with ML infrastructure and tooling, such as feature stores, experiment tracking, model registries, and orchestration frameworks.
- Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn), with strong software engineering fundamentals.
- Experience with CI/CD and ML deployment pipelines, including automated testing, validation, and rollback strategies for ML systems.
- Familiarity with LLM-based systems and GenAI applications, including systems design, evaluation strategies, and observability for non-deterministic systems.
- Strong understanding of LLM architectures and trade-offs, including model selection, latency, cost, and quality optimization.
- Experience with prompt engineering and prompt orchestration, including techniques like few-shot learning, chain-of-thought, and tool/function calling.
- Experience designing and implementing RAG (Retrieval-Augmented Generation) systems, including embedding strategies, vector databases, and retrieval optimization.
- Experience building agentic workflows, including multi-step reasoning, tool use, and orchestration frameworks (e.g., LangChain, LlamaIndex, ADK, or custom frameworks).
- Strong understanding of system design for reliability and scalability, including distributed systems, APIs, and microservices architecture.
- Knowledge of data governance, model governance, and responsible AI practices (security, privacy, bias, explainability).
- Demonstrated ability to translate ambiguous business problems into scalable AI/ML solutions.
- Excellent communication and collaboration skills, with the ability to influence stakeholders and drive cross-functional alignment.
Benefits
- Flexible hybrid work arrangements
- Growth opportunities
- Caring co-workers
- Opportunities to do meaningful work
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