Ai Solution Architect

We are seeking an experienced AI Solution Architect to design, architect, and deliver scalable, secure, and enterprise-ready AI solutions aligned with business objectives. The role will translate business requirements into technical architectures, lead AI solution design, and collaborate with engineering, data, cloud, security, and business teams to deliver production-grade AI capabilities. The ideal candidate has strong expertise in enterprise architecture, cloud platforms, AI/ML technologies, and Generative AI solutions, with the ability to drive technical decisions from conceptual design through implementation and production deployment., Key Roles and Responsibilities

1. AI Solution Architecture and Design o Define end-to-end architecture for enterprise AI, machine learning, and Generative AI solutions. o Translate business requirements and use cases into scalable, secure, and maintainable technical designs. o Evaluate and recommend appropriate AI models, frameworks, platforms, and technology components. o Develop architecture diagrams, solution blueprints, technical specifications, and design documentation.

2. Generative AI and Agentic AI o Design and implement solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and intelligent automation. o Define prompt engin eering strategies, orchestration patterns, tool integrations, and workflow automation. o Establish appropriate AI guardrails, human-in-the-loop controls, and mechanisms to reduce hallucinations. o Evaluate model performance, accuracy, relevance, latency, and cost.

3. Cloud and Platform Architecture o Architect AI solutions on cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform. o Design integrations with enterprise applications, APIs, databases, data platforms, and cloud-native services. o Define infrastructure, scalability, availability, performance, and disaster recovery requirements. o Recommend appropriate deployment approaches, including cloud, hybrid, and on-premises environments.

4. Data Architecture and Integration o Define data ingestion, transformation, processing, storage, and retrieval strategies for AI solutions. o Design data pipelines and integration patterns to support AI and machine learning applications. o Ensure data quality, lineage, governance, and access controls. o Collaborate with data engineering teams to establish reliable and reusable data foundations.

5. Security, Governance, and Responsible AI o Incorporate enterprise security, privacy, and regulatory requirements into AI architecture. o Define controls for sensitive data, personally identifiable information (PII), access management, and secure model interactions. o Establish AI governance practices covering model usage, evaluation, monitoring, and risk management. o Ensure solutions align with enterprise architecture and technology standards.

6. Technical Leadership and Delivery o Provide architectural guidance and technical leadership to development, data, and AI engineering teams. o Review solution designs, code, integrations, and implementation approaches for architectural alignment. o Support proof of concepts (PoCs), prototypes, and technical feasibility assessments. o Resolve complex technical issues and guide teams through implementation and deployment. o Collaborate with product owners, business stakeholders, and delivery teams to ensure solutions meet business and technical expectations.

7. Performance, Monitoring, and Optimization o Define performance benchmarks and success criteria for AI solutions. o Establish monitoring for model quality, response accuracy, latency, availability, and operational costs. o Optimize AI architecture for scalability, reli ability, and cost efficiency. o Support continuous improvement based on production feedback and evolving business needs.

8. Stakeholder Engagement and Innovation o Engage with business stakeholders to identify AI opportunities and assess business value. o Present solution architectures, technical recommendations, and trade-offs to technical and executive audiences. o Stay current with emerging AI technologies, frameworks, and industry best practices. o Identify opportunities to standardize, reuse, and accelerate enterprise AI capabilities. Generic Managerial Skills

• Strong experience in solution architecture, enterprise application architecture, or AI/ML engineering.
• Hands-on experience designing and delivering AI/ML or Generative AI solutions.
• Strong understanding of LLMs, RAG, AI agents, prompt engineering, and model evaluation.
• Experience with Python and AI/ML frameworks such as PyTorch, TensorFlow, or equivalent.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Understanding of APIs, microservices, data pipelines, databases, and enterprise integration patterns.
• Knowledge of AI security, data privacy, governance, and responsible AI practices.
• Strong communication, stakeholder management, problem-solving, and technical leadership skills.
• Experience implementing enterprise AI solutions in production environments.
• Familiarity with vector databases, embeddings, semantic search, and orchestration frameworks.
• Experience with MLOps, CI/CD, model monitoring, and cloud infrastructure automation.
• Experience with enterprise platforms such as Salesforce, SAP, ServiceNow, or other business applications.
• Relevant cloud architecture or AI certifications., Strong stakeholder management and ability to communicate confidently with senior client and TCS leadership.
• Sound decision-making and risk-management judgment under time pressure, particularly during CAB and emergency change scenarios. Salary Range: $180,000-$220,000 a year