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Sr Solution Architect
Tata Consultancy Services
Dubai, UAE
Full Time
Senior
2 weeks ago
Agentic AIAzure OpenAIAzure AI SearchPythonKubernetesRAG Architecture
Free
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Agentic AIAzure OpenAIAzure AI Search
About the Role
Tata Consultancy Services is seeking a Senior Solution Architect to design and build core components of an agentic AI platform (GERNAS OS) for banking domains. The role requires deep expertise in Azure AI services, LLM engineering, enterprise integration, and AI governance.
Key Skills for This Role
Agentic AIAzure OpenAIAzure AI SearchPythonKubernetesRAG Architecture
Responsibilities
- Design and build core components of GERNAS OS including agent runtime, orchestration, memory, model routing, tool integration, agent registry, reusable agent services, and multi agent orchestration patterns
- Engineer solutions using Azure OpenAI, Azure AI Search, embeddings, Azure Document Intelligence, vector stores, API gateways, and model management layers
- Embed Responsible AI principles into the platform lifecycle including fairness, safety, privacy, accountability, transparency, explainability, and human in the loop controls
- Design and implement secure tool orchestration and MCP gateway patterns for governed interaction between AI agents and enterprise systems
- Build and operate platform observability covering traces, prompts, tool calls, latency, cost, token usage, model performance, and agent behavior
- Drive DevOps, MLOps, LLMOps, and AIOps practices including CI/CD pipelines, environment management, release governance, monitoring dashboards, benchmarking, evaluation pipelines, and production support
- Partner with business, technology, risk, compliance, and vendor teams to assess AI use cases and convert them into executable platform delivery plans
- Work with external vendors and strategic partners to review solution architecture, delivery plans, implementation quality, technical documentation, and platform alignment
- Mentor AI engineers, platform engineers, cloud engineers, data scientists, and DevOps engineers on enterprise grade agentic AI delivery
- Contribute to internal knowledge assets such as enterprise agent frameworks, runbooks, reference architectures, and developer guidance
Requirements
- Bachelor's or Master's degree in Computer Science, AI, Data Science, Engineering, Information Systems, or related field
- 13+ years of hands on experience in technology with strong exposure to AI/ML platforms, cloud engineering, enterprise architecture, or platform engineering
- 3+ years of direct experience with Generative AI, LLMs, RAG, embeddings, vector search, AI agents, or multi agent orchestration in enterprise or regulated environments
- Experience working with large scale production AI systems and real time enterprise data
- Strong programming skills in Python with experience in AI/ML libraries, LLM SDKs, REST APIs, and enterprise integration frameworks
- Hands on expertise with Azure OpenAI, Azure AI Search, Azure Document Intelligence, embeddings, vector stores, API Management, service principals, identity, and secure endpoint integration
- Experience in designing and deploying RAG patterns, vector search, embedding pipelines, and knowledge retrieval systems at enterprise scale
- Expertise in cloud native architecture, Kubernetes/AKS, containerized deployments, API gateway patterns, event driven architecture, and infrastructure as code
- Experience with CI/CD pipelines, Git based engineering workflows, release governance, environment management, model versioning, and automated testing for AI systems
- Proficiency in AI observability tools (e.g., Opik, Langfuse, Arize), tracing, logging, monitoring dashboards, benchmarking, evaluation pipelines, and operational alerting
- Deep knowledge of Responsible AI principles, guardrails, prompt defense, PII protection, audit trails, model monitoring, human in the loop controls, and regulatory compliance in AI systems
- Cloud, AI, architecture, or security certifications preferred (e.g., Azure AI Engineer Associate, Azure Solutions Architect Expert, Certified Kubernetes Administrator, TOGAF)
Full Job Posting
About Us
- Tata Consultancy Services (TCS) is an IT services, consulting and business solutions organization partnering with many of the world's largest businesses for over 50 years.
- TCS offers a consulting led, cognitive powered, integrated portfolio of business, technology and engineering services and solutions delivered through its Location Independent Agile delivery model.
Key Accountabilities
- Design and build core components of GERNAS OS including agent runtime, orchestration, memory, model routing, tool integration, agent registry, reusable agent services, and multi agent orchestration patterns.
- Engineer solutions using Azure OpenAI, Azure AI Search, embeddings, Azure Document Intelligence, vector stores, API gateways, and model management layers.
- Embed Responsible AI principles into the platform lifecycle including fairness, safety, privacy, accountability, transparency, explainability, and human in the loop controls.
- Design and implement secure tool orchestration and MCP gateway patterns enabling governed interaction between AI agents and enterprise systems.
- Build and operate platform observability covering traces, prompts, tool calls, latency, cost, token usage, model performance, and agent behavior.
- Drive DevOps, MLOps, LLMOps, and AIOps practices including CI/CD pipelines, environment management, release governance, monitoring dashboards, benchmarking, evaluation pipelines, and production support.
- Partner with business, technology, risk, compliance, and vendor teams to assess AI use cases and convert them into executable platform delivery plans.
- Work with external vendors and strategic partners to review solution architecture, delivery plans, implementation quality, technical documentation, and platform alignment.
- Mentor AI engineers, platform engineers, cloud engineers, data scientists, and DevOps engineers on enterprise grade agentic AI delivery.
- Contribute to internal knowledge assets such as enterprise agent frameworks, runbooks, reference architectures, and developer guidance.
Minimum Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, Information Systems, or a related field.
- Cloud, AI, architecture, or security certifications are preferred (e.g., Azure AI Engineer Associate, Azure Solutions Architect Expert, Certified Kubernetes Administrator, TOGAF).
Minimum Experience
- 13+ years of hands on experience in technology, with strong exposure to AI/ML platforms, cloud engineering, enterprise architecture, or platform engineering.
- 3+ years of direct experience with Generative AI, LLMs, RAG, embeddings, vector search, AI agents, or multi agent orchestration in enterprise or regulated environments.
- Experience working with large scale production AI systems and real time enterprise data.
Technical Skills
- Proficiency in designing and building multi agent systems, agent orchestration, memory management, model routing, tool integration, and agent lifecycle management using frameworks such as AutoGen, Semantic Kernel, LangChain, CrewAI, or LangGraph.
- Strong programming skills in Python, with extensive experience in AI/ML libraries, LLM SDKs, REST APIs, and enterprise integration frameworks.
- Hands on expertise with Azure OpenAI, Azure AI Search, Azure Document Intelligence, embeddings, vector stores, API Management, service principals, identity, and secure endpoint integration.
- Experience in designing and deploying Retrieval Augmented Generation patterns, vector search, embedding pipelines, and knowledge retrieval systems at enterprise scale.
- Expertise in cloud native architecture, Kubernetes/AKS, containerized deployments, API gateway patterns, event driven architecture (Event Hub, Service Bus, Kafka, Solace), and infrastructure as code.
- Experience with CI/CD pipelines, Git based engineering workflows, release governance, environment management, model versioning, and automated testing for AI systems.
- Proficiency in AI observability tools (e.g., Opik, Langfuse, Arize), tracing, logging, monitoring dashboards, benchmarking, evaluation pipelines, and operational alerting.
- Deep knowledge of Responsible AI principles, guardrails, prompt defense, PII protection, audit trails, model monitoring, human in the loop controls, and regulatory compliance in AI systems.
Knowledge, Skills, & Experience
- Strong understanding of enterprise AI platform design, agentic AI architecture, orchestration patterns, and ability to translate complex business problems into scalable, reusable platform capabilities.
- Deep understanding of AI governance, Responsible AI, model risk, data governance, information security, and compliance requirements in a regulated banking environment.
- Excellent analytical and problem solving skills with ability to design end to end AI agent architectures, evaluate trade offs, and make pragmatic technology decisions at enterprise scale.
- Strong verbal and written communication skills, capable of explaining complex AI architecture to non technical stakeholders, preparing board ready presentations, and supporting executive level discussions.
- Proven ability to work effectively across cross functional teams, collaborating with AI engineers, data scientists, cloud engineers, DevOps, business leaders, risk, compliance, enterprise architecture, and external vendors.
- Experience managing vendor relationships, reviewing architecture deliverables, driving delivery accountability, and ensuring external solutions align with enterprise platform standards and governance controls.
- Willingness to stay updated on the latest AI technologies, agentic frameworks, and cloud services, and ability to apply them to solve evolving enterprise challenges in a fast moving banking environment.
Application Details
- Application Deadline: 30 July 2026
- Privacy Note: https://www.tcs.com/connect with tcs/privacy policy
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