AI Architect

Roles & Responsibilities

Key Responsibilities

Enterprise AI Architecture

Define end-to-end architecture for Generative AI, Agentic AI, machine learning, and intelligent automation solutions. Translate airline business priorities into scalable AI capabilities, reference architectures, solution patterns, and implementation roadmaps. Design reusable AI services across digital channels, airline operations, IT operations, customer service, engineering, and enterprise functions. Establish architecture standards for model integration, orchestration, data access, APIs, security, observability, evaluation, and deployment. Review solution designs and ensure alignment with enterprise standards and target architecture.

Generative AI and Agentic AI

Architect enterprise-grade LLM solutions using RAG, knowledge grounding, tool integration, and multi-agent orchestration. Design autonomous and human-in-the-loop workflows with clear controls, approvals, escalations, and auditability. Define patterns for agent planning, reasoning, state management, memory, function calling, structured outputs, and secure tool access. Evaluate frameworks such as LangGraph, LangChain, Semantic Kernel, CrewAI, or equivalent enterprise technologies. Design reusable components for prompts, tools, workflows, model gateways, evaluation, guardrails, and agent observability.

Airline Business and Operational Solutions

Partner with airline business and technology teams to identify and prioritize high-value AI opportunities. Architect solutions for digital customer experience, personalization, operational reliability, disruption management, reservations, customer service, employee assistance, major incident management, intelligent IT operations, engineering productivity, and knowledge management. Design AI capabilities for high-volume, near-real-time, customer-facing, and operationally critical environments. Balance innovation and speed with availability, reliability, safety, security, and operational stability.

Data, Context, and Knowledge Architecture

Design secure data and knowledge architectures that ground AI solutions in trusted enterprise information. Define patterns for ingestion, chunking, metadata, embeddings, vector search, reranking, retrieval, response validation, and knowledge freshness. Integrate structured, unstructured, streaming, and operational data through enterprise data platforms, APIs, and event streams. Partner with data teams to ensure quality, lineage, access control, privacy, and governance for information used by AI systems.

AWS Cloud and AI Platforms

Architect AI solutions using Amazon Bedrock, SageMaker, OpenSearch, S3, EKS/ECS, Lambda, Step Functions, API Gateway, Glue, Athena, Redshift, Kinesis/MSK, IAM, KMS, Secrets Manager, and CloudWatch. Evaluate models and services based on quality, security, latency, scalability, portability, reliability, and cost. Design cloud-native AI platforms that support experimentation, controlled de ployment, enterprise reuse, and model choice. Integrate third-party and open-source models where appropriate while maintaining enterprise security and governance.

AI Engineering and Integration

Provide hands-on architecture guidance for Python-based AI services, APIs, microservices, and event-driven applications. Define secure integration patterns for agents to interact with enterprise applications, APIs, databases, and operational tools. Establish standards for schema validation, exception handling, retries, fallbacks, rate limits, and human escalation. Guide teams in building modular, testable, reusable, and maintainable AI components.

LLMOps, MLOps, Observability, and Production Readiness

Define lifecycle standards for model selection, prompt management, training, fine-tuning, testing, deployment, monitoring, versioning, and retirement. Establish CI/CD and automated testing for models, prompts, retrieval pipelines, agents, APIs, and supporting services. Design evaluation frameworks covering accuracy, groundedness, relevance, safety, latency, reliability, operational impact, and cost. Implement end-to-end tracing and observability for model calls, retrieval decisions, agent execution, tool usage, and failures. Define production-readiness criteria, rollback approaches, service objectives, support models, and incident-response procedures.

Responsible AI, Security, and Governance

Embed responsible AI, privacy, cybersecurity, compliance, and risk controls into architecture and delivery. Define identity, authorization, encryption, data isolation, auditability, secrets management, and sensitive-data protection controls. Implement guardrails for prompt injection, hallucination, data leakage, unsafe output, and unauthorized tool execution. Ensure traceability and appropriate human oversight for high-impact or operationally sensitive AI recommendations and actions.

Technical Leadership and Collaboration

Serve as a trusted AI architecture advisor to business and technology leadership. Lead architecture reviews, technical workshops, design sessions, and AI use-case assessments. Provide clear recommendations on technology selection, implementation approach, risks, dependencies, and trade-offs. Mentor AI engineers, data scientists, software engineers, and solution architects. Create reference architectures, reusable patterns, standards, playbooks, and contributions to the enterprise AI roadmap.

Salary Range-$100,000-$150,000 a year

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TCS Employee Benefits Summary:

Discretionary Annual Incentive.

Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.

Family Support: Maternal & Parental Leaves.

Insurance Options: Auto & Home Insurance, Identity Theft Protection.

Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement.a

Time Off: Vacation, Time Off, Sick Leave & Holidays.

Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.