Solutions Architect

Job summary

Own the architecture and productionization of AI and generative AI solutions, turning proofs of concept into scalable enterprise products. Design and integrate AI pipelines and services, and establish evaluation, governance, security, deployment, and production observability practices.

Job details

Inclusion without Exception Tata Consultancy Services (TCS) is an equal opportunity employer, and embraces diversity in race, nationality, ethnicity, gender, age, physical ability, neurodiversity, and sexual orientation, to create a workforce that reflects the societies we operate in. Our continued commitment to Culture and Diversity is reflected in our people stories across our workforce and implemented through equitable workplace policies and processes. TCS has been serving the Canadian marketplace for more than 35 years and today is the technology partner of choice for many of the country's leading organizations. As one of the largest IT services providers in Canada and globally, TCS is aiming to become the world's largest AI-led technology services company and is enabling its clients to transform themselves across the full AI stack, from infrastructure to intelligence. Rooted in the heritage and values of the Tata Group, TCS is focused on creating long-term value for its clients, investors, employees, and the communities it serves. With a highly skilled workforce supporting customers from coast to coast, TCS has consistently been recognized as a Top Employer by the Top Employers Institute, including a recent top ranking at the country level. TCS also sponsors 14 of the world's most prestigious marathons and endurance events, including the TCS Toronto Waterfront Marathon, reflecting its commitment to health, sustainability, and community Role Description • Own the end-to-end architecture and productionization of AI/GenAI solutions. • Convert AI/GenAI PoCs into scalable, enterprise-grade production products. • Design RAG, LLM, agentic AI and ML architectures based on business use cases. • Define model selection strategy considering accuracy, latency, scalability, security and cost. • Design data ingestion, embeddings, vector database, retrieval and reranking pipelines. • Establish LLMOps/MLOps and CI/CD processes for automated testing, deployment and rollback. • Define AI evaluation frameworks covering accuracy, hallucination, faithfulness, retrieval quality and business KPIs. • Integrate AI services with enterprise applications, APIs, databases and data platforms. • Implement security, PII protection, access controls, auditability and Responsible AI governance. • Define human-in-the-loop and exception-management workflows for high-risk AI decisions. • Establish production monitoring, tracing, logging, performance and cost observability. Salary Range - CA$ 100,000 - CA$ 130,000 Per Year Note: TCS does not use artificial intelligence tools for candidate screening or evaluation. This posting is for a current vacancy The hiring process includes an initial screening by the TCS Hiring Team, followed by a technical evaluation and managerial discussion conducted by the Business Team, and concluding with the final HR evaluation. Tata Consultancy Services Canada Inc. is committed to meeting the accessibility needs of all individuals in accordance with the Accessibility for Ontarians with Disabilities Act (AODA) and the Ontario Human Rights Code (OHRC). Should you require accommodation during the recruitment and selection process, please inform Human Resources. Thank you for your interest in TCS. Candidates that meet the qualifications for this position will be contacted within a 2-week period. We invite you to continue to apply for other opportunities that match your profile.

What you’ll do

Own the architecture and productionization of AI and generative AI solutions, turning proofs of concept into scalable enterprise products. Design and integrate AI pipelines and services, and establish evaluation, governance, security, deployment, and production observability practices.

Requirements

The role calls for expertise designing RAG, LLM, agentic AI, and machine learning architectures, including data ingestion, embedding, retrieval, and reranking pipelines. It also requires the ability to establish LLMOps/MLOps, evaluation, security, Responsible AI, human-in-the-loop, and monitoring practices; no specific education or years of experience are stated.

Listed skills

• CI/CD · Preferred
• Machine learning · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• AI Architecture
• Generative AI
• Retrieval-Augmented Generation
• Large Language Models
• Agentic AI
• Machine Learning
• Model Selection
• Data Ingestion
• Embeddings
• Vector Databases
• Retrieval and Reranking
• LLMOps
• MLOps
• CI/CD
• AI Evaluation
• Responsible AI Governance

Job areas

• Technology
• Software
• Engineering
• Data & Analytics
• Consulting

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