Enterprise Architect - Aldie, VA | TCS Job

Proven experience driving AI strategy and implementation across multiple functions

Ability to engage CXO/VP-level stakeholders and influence decision-making

Strong program management skills to oversee AI roadmap, execution, governance

Expertise in end-to-end AI lifecycle (use case>data>model>deployment>monitoring>optimization)

Understanding of ethical AI, regulatory compliance, data privacy norm

Experience in setting up AI Centers of Excellence (CoE)

Certifications in AI/ML, cloud, or enterprise architecture

Ability to translate business challenges into AI-driven solutions

Strong communication, consulting, and change management capability

Strong Knowledge of below technologies and hands-on experience in building AI solutions using them:

Generative AI (LLMs, RAG, Agentic AI)

AI/ML Architecture & Solution Design

Python ecosystem (PyTorch, TensorFlow, FastAPI, LangChain, LangGraph, etc.)

Cloud AI platforms (Azure OpenAI, AWS Bedrock, GCP Vertex AI)

Data Engineering & Vector Databases (FAISS, Pinecone, etc.).

Expertise in MLOps / LLMOps / GenAI Ops (model versioning, deployment pipelines, monitoring) 

Production grade enterprise Implementations including: o Strong understanding of enterprise data architecture (data pipelines, feature stores, knowledge grounding, vector DBs) o Experience integrating AI solutions into enterprise ecosystems (APIs, microservices, OSS/BSS/CRM systems) 

Knowledge of Responsible AI principles (bias mitigation, explainability, auditability, regulatory compliance) 

Strong awareness of AI security (prompt injection, data privacy, access controls) 

Good-to-Have 

Experience in automation, AIOps, MLOps frameworks 

Experience in cost optimization of AI workloads 

Experience in industry-specific AI use cases 

Responsibilities and Expectations from the Role

AI Strategy, Advisory & Opportunity Identification 

Engage with client stakeholders to understand business challenges and identify AI/GenAI opportunities 

Shape AI strategy, use cases, and roadmap aligned to client objectives and deal strategy 

Provide advisory on AI adoption, readiness, and transformation approach Solution Architecture & Presales Solutioning 

Design enterprise-grade AI/GenAI solution architectures tailored to client needs 

Define integration patterns with existing enterprise ecosystems (APIs, microservices, OSS/BSS/CRM) 

Select appropriate AI platforms, tools, and accelerators aligned to solution requirements 

Collaborate with CoEs, domain teams, and account teams to build comprehensive solution offerings POC Development & Deal Acceleration 

Develop rapid PoCs, prototypes, and demonstrations to validate AI solution feasibility

Showcase business value through working demos, use-case walkthroughs, and client presentations 

Support RFP responses, solution bids, and technical discussions during deal cycles 

Accelerate deal closure by de-risking solution approaches through demonstrable outcomes AI Enablement & Handover to Account Teams 

Create reusable solution blueprints, architecture documents, and implementation guidelines 

Ensure struct ured knowledge transfer of AI solutions, POCs, and architecture to account/delivery teams 

Support accounts during transition from presales to delivery by clarifying solution intent and design decisions 

Enable continuity by ensuring accounts are equipped to implement and scale the solution Collaboration & Ecosystem Engagement 

Work closely with account teams, delivery units, AI CoEs, and domain SMEs 

Orchestrate contributions across multiple teams to build end-to-end AI solutions 

Facilitate alignment between business, engineering, and architecture stakeholders Innovation, Reusability & Thought Leadership 

Stay updated with evolving AI/GenAI technologies and assess applicability 

Build reusable accelerators, frameworks, and solution assets 

Conduct workshops, client demos, and knowledge-sharing sessions AI Lifecycle Guidance (Advisory) 

Guide teams on AI lifecycle best practices (data, model, deployment, monitoring) 

Recommend MLOps / LLMOps approaches for scalable execution 

Advise on performance, cost optimization, and scalability considerations 

Responsible AI, Risk & Compliance Advisory 

Provide guidance on Responsible AI principles, security, and compliance considerations 

Identify risks and recommend mitigation approaches (data privacy, model risks, prompt security) 

Vendor & Platform Advisory 

Evaluate AI platforms, tools, and partner ecosystems for suitability 

Recommend build vs buy decisions aligned to client context

Qualifications: 

Bachelor’s degree in information technology, Business or related field. MBA’s can also be preferred  

Experience in 

AI/GenAI Solution Architecture 

Enterprise AI Strategy & Governance 

Python & AI/ML Frameworks 

Experience in Cloud AI Platforms 

5+ Years in AI/ML/GenAI

Salary Range: 150000-180000 a year

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