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TC
Tata Consultancy Services Limited
AI Architect
Career Insights for Artificial Intelligence Engineer (General)
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Based on Maryland data
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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
AI Architect
Must Have Technical/Functional Skills
- 12+ years of technology architecture experience with 4+ years in AI/ML, GenAI, LLM and enterprise solution architecture.
- Hands-on experience designing and implementing Anthropic Claude based solutions, preferably through Amazon Bedrock, including prompt engineering, tool/function calling, agent orchestration and evaluation.
- Strong AWS architecture experience across Bedrock, Lambda, API Gateway, ECS/EKS, Step Functions, S3, IAM, KMS, VPC, CloudWatch, CloudTrail and CI/CD pipelines.
- Experience building agentic AI patterns including planning, tool use, memory, guardrails, approvals, HITL workflows, exception management and observability.
- Proven experience with RAG architecture, vector databases/semantic search, embeddings, knowledge ingestion, document processing and enterprise data integration.
- Good understanding of AI governance, security, privacy, model risk, responsible AI, data leakage controls and auditability in regulated financial services environments.
- Functional exposure to Asset Management / Investment Operations / Distribution / Client Reporting / Middle Office processes is strongly preferred.
- Ability to integrate AI solutions with in-house platforms, APIs, workflow tools, data platforms and enterprise identity/access management capabilities.
- Lead end-to-end architecture, design and implementation of Claude based Agentic AI solutions on AWS cloud and enterprise internal platforms.
- Partner with business, domain SMEs, security, cloud, data and engineering teams to translate use cases into scalable technical architectures and delivery roadmaps.
- Define reference architecture, solution patterns, NFRs, integration approach, guardrails, DevSecOps model, deployment strategy and operating model.
- Design agent workflows, prompt/tool frameworks, RAG pipelines, orchestration layers, human review checkpoints and exception handling patterns.
- Drive PoCs/MVPs through production readiness including performance, reliability, cost optimization, monitoring, testing and AI evaluation frameworks.
- Ensure compliance with enterprise security, privacy, model governance, audit, IAM, encryption, logging and data residency requirements.
- Provide technical leadership to onsite/offshore engineering teams, conduct design reviews, resolve blockers and ensure engineering quality.
- Create architecture artifacts, executive walkthroughs, design documents, backlog epics/stories, estimation inputs and implementation plans.
- Strong stakeholder management and executive communication skills with ability to simplify complex AI architecture for business and technology audiences.
- Ability to lead cross-functional teams across business, cloud, data, security, architecture and delivery functions.
- Experience managing ambiguity, prioritizing use cases, defining MVP scope and driving outcomes in a fast-paced client environment.
- Strong estimation, planning, risk management, dependency management and issue resolution skills.
- Ability to mentor engineers, establish best practices and drive adoption of reusable AI engineering patterns.
- Excellent written communication, presentation, documentation and governance reporting skills.