Find Jobs
Find Jobs Near You – Available Work in Your Location
Skip to job details
TC
Tata Consultancy Services Limited
Lead AI Architect
Career Insights for Artificial Intelligence Engineer (General)
See where this job fits in the broader career landscape. Knowing your career path helps you see what's possible from here.
Scorecard
Based on Maryland data
Review key factors to help you decide if this role fits your goals. How is this calculated?
What they do
An Artificial Intelligence Engineer develops, tests, and deploys artificial intelligence models. May work closely with data software engineers and data professionals to train and implement AI models into existing systems or develop new applications.
$130,270 / year median in Maryland
Job Description
Must Have Technical/Functional Skills
- 15+ years of enterprise architecture, solution architecture and cloud architecture experience, with 5+ years leading AI/ML, GenAI or enterprise platform architecture programs.
- Proven ability to lead architecture discussions, technology decisions and design governance for Anthropic Claude based Agentic AI solutions, preferably on Amazon Bedrock.
- Deep understanding of AI architecture patterns including agent orchestration, multi-agent systems, RAG, vector databases, tool/function calling, MCP, guardrails, HITL and LLM evaluation.
- Strong AWS architecture knowledge across Bedrock, Lambda, API Gateway, Step Functions, ECS/EKS, S3, IAM, KMS, VPC, CloudWatch, CloudTrail, CI/CD and DevSecOps.
- Experience defining enterprise AI reference architectures, reusable patterns, architecture governance, NFRs, decision records, and target-state roadmaps.
- Strong understanding of AI governance, Responsible AI, model risk, security, privacy, data governance, auditability and compliance requirements in financial services.
- Ability to guide AI Architects, FDEs and engineering teams on design, scalability, resiliency, integration, observability, performance and cost optimization.
- Experience integrating AI platforms with enterprise applications, APIs, workflow systems, identity/access management, data platforms and in-house capabilities.
- Working knowledge of Asset Management, Investment Operations, Distribution, Client Reporting and Middle Office processes is preferred. Roles & Responsibilities
- Own overall technical architecture direction and decision-making for Claude based Agentic AI implementation across the enterprise program.
- Lead architectural discussions with business stakeholders, enterprise architects, security, data, cloud, platform and engineering teams.
- Define target-state architecture, reference patterns, integration approach, guardrails, NFRs, deployment model and operating model for enterprise AI solutions.
- Chair design reviews and architecture governance forums; drive architecture decisions, trade-off analysis, risk mitigation and solution approvals.
- Complement AI Architects by providing enterprise-level architecture guardrails, cross-workstream alignment, technical governance and escalation support.
- Guide architecture teams and technical teams on implementation approaches, reusable components, coding standards, testing strategy and production readiness.
- Partner with domain architects and business teams to ensure AI solution designs align with business value, operating model needs and adoption priorities.
- Oversee architecture for agent workflows, RAG pipelines, knowledge ingestion, tool/API integrations, observability, LLMOps, security and AI FinOps.
- Ensure solutions meet enterprise standards for security, IAM, encryption, privacy, audit logging, model governance, data controls and regulatory compliance.
- Provide executive architecture updates, decision logs, technical roadmaps, delivery risks and recommendations to senior stakeholders. Generic Managerial Skills, If any
- Strong executive stakeholder management and ability to influence architecture decisions across business, technology and risk stakeholders.
- Experience leading architects, senior engineers and cross-functional technical teams in complex transformation programs.
- Ability to build consensus, resolve conflicts and make crisp architecture decisions in ambiguous environments.
- Strong strategic thinking, technical judgment, risk management and dependency management skills.
- Ability to mentor AI Architects and engineering leads while maintaining delivery discipline and architecture consistency.
- Excellent communication, presentation, documentation and governance reporting skills for senior leadership audiences.