Senior AI Architect

Tata Consultancy Services Limited; Oaks (PA); On-site; USD 110,000 - 130,000; Full time

Benefits offered by this job

Discretionary annual incentive

Medical coverage

Maternal & parental leaves

401K plan

Performance bonus

Training reimbursement

Vacation & holidays

Identity theft protection

Commuter benefits

Legal assistance

Pet insurance

College fund

Student loan refinancing

Job summary

Tata Consultancy Services Limited is seeking an experienced Enterprise AI Architect to define end-to-end AI and GenAI architectures for large-scale deployments. You will establish reference patterns for LLM/SLM, RAG, and traditional ML, while evaluating models for accuracy, latency, security, and cost.

Lead architecture reviews and guide engineering teams across security, data, and governance. Strong hands-on with cloud AI platforms (Azure AWS Google Cloud) and API-rich enterprise integrations

Qualifications

• Extensive enterprise AI/GenAI architecture experience.
• Hands-on with LLMs, embeddings, RAG, vector databases, AI agents.
• Experience with Azure AI, AWS, or Google Cloud.
• Understanding of commercial and open-source AI models.
• Strong knowledge of APIs, microservices, containers, cloud security.
• Ability to evaluate trade-offs across quality, performance, cost.
• Define end-to-end architecture for enterprise AI and GenAI.
• Develop reference architectures and reusable patterns.
• Evaluate models on accuracy, latency, security, scalability.
• Translate business use cases into scalable AI solutions.

Responsibilities

• Define end-to-end architecture for enterprise AI and GenAI solutions.
• Establish reference architectures and reusable patterns for LLM, SLM, RAG, agentic AI, and traditional ML solutions.
• Evaluate AI models and platforms based on business requirements, accuracy, latency, security, scalability, and cost.
• Define criteria for determining when to use AI, what type of AI to use, and when traditional technology or automation is more appropriate.
• Develop strategies for model routing, token optimization, caching, prompt optimization, and inference-cost management.
• Architect secure integration of AI solutions with enterprise applications, APIs, data platforms, and knowledge repositories.
• Provide architecture guidance for AI agents, orchestration, vector databases, embeddings, APIs, and model gateways.
• Collaborate with security, data, infrastructure, governance, and application architecture teams.
• Provide technical leadership to engineering teams and conduct architecture/design reviews.
• Help translate business use cases into scalable AI solution architectures.

Skills

AI Architecture

GenAI

LLMs

Embeddings

Vector databases

AI agents

Prompt engineering

Model evaluation

Cloud platforms

APIs & microservices

Security & enterprise integration

Education

Bachelor of Computer Science

Tools

Azure AI

AWS

Google Cloud

Job description

Job Description

• Strong enterprise solution architecture experience with significant exposure to AI/ML and GenAI.
• Hands-on understanding of LLMs, SLMs, RAG, embeddings, vector databases, AI agents, prompt engineering, and model evaluation.
• Experience with cloud AI platforms such as Azure AI, AWS, or Google Cloud.
• Understanding of commercial and open-source AI models.
• Strong knowledge of APIs, microservices, containers, cloud architecture, security, and enterprise integration.
• Ability to evaluate architectural trade-offs across quality, performance, risk, scalability, and cost.
• Define end-to-end architecture for enterprise AI and GenAI solutions.
• Establish reference architectures and reusable patterns for LLM, SLM, RAG, agentic AI, and traditional ML solutions.
• Evaluate AI models and platforms based on business requirements, accuracy, latency, security, scalability, and cost.
• Define criteria for determining when to use AI, what type of AI to use, and when traditional technology or automation is more appropriate.
• Develop strategies for model routing, token optimization, caching, prompt optimization, and inference-cost management.
• Architect secure integration of AI solutions with enterprise applications, APIs, data platforms, and knowledge repositories.
• Provide architecture guidance for AI agents, orchestration, vector databases, embeddings, APIs, and model gateways.
• Collaborate with security, data, infrastructure, governance, and application architecture teams.
• Provide technical leadership to engineering teams and conduct architecture/design reviews.
• Help translate business use cases into scalable AI solution architectures.

Must Have Technical/Functional Skills

• Strong enterprise solution architecture experience with significant exposure to AI/ML and GenAI.
• Hands-on understanding of LLMs, SLMs, RAG, embeddings, vector databases, AI agents, prompt engineering, and model evaluation.
• Experience with cloud AI platforms such as Azure AI, AWS, or Google Cloud.
• Understanding of commercial and open-source AI models.
• Strong knowledge of APIs, microservices, containers, cloud architecture, security, and enterprise integration.
• Ability to evaluate architectural trade-offs across quality, performance, risk, scalability, and cost.
• Define end-to-end architecture for enterprise AI and GenAI solutions.
• Establish reference architectures and reusable patterns for LLM, SLM, RAG, agentic AI, and traditional ML solutions.
• Evaluate AI models and platforms based on business requirements, accuracy, latency, security, scalability, and cost.
• Define criteria for determining when to use AI, what type of AI to use, and when traditional technology or automation is more appropriate.
• Develop strategies for model routing, token optimization, caching, prompt optimization, and inference-cost management.
• Architect secure integration of AI solutions with enterprise applications, APIs, data platforms, and knowledge repositories.
• Provide architecture guidance for AI agents, orchestration, vector databases, embeddings, APIs, and model gateways.
• Collaborate with security, data, infrastructure, governance, and application architecture teams.
• Provide technical leadership to engineering teams and conduct architecture/design reviews.
• Help translate business use cases into scalable AI solution architectures.

Roles & Responsibilities

• Define end-to-end architecture for enterprise AI and GenAI solutions.
• Establish reference architectures and reusable patterns for LLM, SLM, RAG, agentic AI, and traditional ML solutions.
• Evaluate AI models and platforms based on business requirements, accuracy, latency, security, scalability, and cost.
• Define criteria for determining when to use AI, what type of AI to use, and when traditional technology or automation is more appropriate.
• Develop strategies for model routing, token optimization, caching, prompt optimization, and inference-cost management.
• Architect secure integration of AI solutions with enterprise applications, APIs, data platforms, and knowledge repositories.
• Provide architecture guidance for AI agents, orchestration, vector databases, embeddings, APIs, and model gateways.
• Collaborate with security, data, infrastructure, governance, and application architecture teams.
• Provide technical leadership to engineering teams and conduct architecture/design reviews.
• Help translate business use cases into scalable AI solution architectures.

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.

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

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

Salary Range $110,000 - $130,000 a year

Qualifications

BACHELOR OF COMPUTER SCIENCE