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
#LI-KR3
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.
