Role: Enterprise Architect
Required Skills: Artificial Intelligence
Experience: 10 - 15 Years
Function: TECHNOLOGY
Apply By: 2026-12-21
Must Have Technical/Functional Skills
Data, AI and conversational analytics - core of the role
- Strong consulting and solution architecture experience spanning modern data platforms, Data Engineering, Data Management, analytics, Generative AI and conversational analytics.
- Working knowledge of Data Engineering using Python, SQL, Spark / PySpark, ETL / ELT, data pipelines, batch and streaming patterns, APIs, Kafka and cloud-native orchestration services.
- Working knowledge of Data Management, including data architecture, lakehouse and medallion patterns, Data Mesh, Master Data Management, metadata, cataloging, data quality, lineage, security, privacy and governance.
- Hands-on understanding of conversational analytics and natural-language access to enterprise data, including text-to-SQL, semantic layers, RAG, enterprise search, chat-based BI, insight generation and responsible access controls.
- Deep understanding of GenAI engineering: RAG over enterprise data, prompt and context engineering, embeddings and vector stores such as Pinecone, Weaviate or Milvus, model selection and LLM fine-tuning using LoRA / QLoRA.
- Practical experience with agentic AI patterns including planning, tool use, function calling, memory and multi-agent orchestration using LangChain, LangGraph, Semantic Kernel or equivalent.
- Knowledge of classical ML and data science using Python, PyTorch / TensorFlow and scikit-learn, including feature engineering, model evaluation and interpretability.
- Production awareness across DataOps, MLOps and LLMOps: CI/CD, testing, observability, model and data monitoring, drift, cost management, Docker and Kubernetes.
Cloud data and AI platform foundation
- Strong expertise in at least one cloud data and AI stack, with familiarity in a second: Google Cloud (BigQuery, Dataflow, Dataproc, Dataplex, Vertex AI), Azure (Data Factory, Databricks, Fabric, Purview, Azure ML, Azure OpenAI) or AWS (Glue, EMR, Redshift, SageMaker, Bedrock).
- Experience with Databricks and/or Snowflake, including lakehouse or warehouse architecture, data engineering, governance and AI capabilities such as Mosaic AI or Cortex.
- Ability to design an AI-ready data estate with scalable ingestion, transformation, semantic and serving layers, backed by data quality, lineage, governance and observability.
Consulting, CoE leadership and solutioning
- Proven experience leading or building a Data and AI practice, CoE or capability pod, defining offerings and assets, setting architecture standards, and mentoring data, analytics and AI teams.
- Strong customer-facing track record in discovery and design workshops, use-case prioritization, current-state assessment, roadmap creation and executive presentations.
- Ownership of RFP / RFI and proactive Data and AI proposals covering solution approach, target architecture, estimation, delivery model, commercials inputs and orals.
- Experience leading initiatives from assessment and PoC through pilot and production with distributed onsite-offshore teams.
Domain context
- Experience with high-technology customers including hyperscalers, software and platform companies, semiconductor organizations and consumer-electronics enterprises.
Roles & Responsibilities
We are seeking a Data and AI Consultant for a newly formed Data & Analytics unit serving high-technology customers on the US West Coast. This is a senior, customer-facing role that combi nes advisory and solutioning leadership with practical knowledge across Data Engineering, Data Management, conversational analytics, advanced analytics and AI. The consultant will shape account-level Data and AI agendas, identify opportunities, develop roadmaps and work with CoE and delivery teams to take solutions into production.
Key Responsibilities:
- Data and AI advisory: Build trusted relationships with customer data, analytics, AI and platform leaders; assess business and technology priorities; and define practical transformation roadmaps.
- Data Engineering and platform solutioning: Shape modern data architectures and engineering solutions covering ingestion, transformation, lakehouse / warehouse, streaming, integration and data serving.
- Data Management: Advise on governance, metadata, cataloging, quality, lineage, Master Data Management, security and privacy to make enterprise data trusted and AI-ready.
- Conversational analytics: Identify and solution natural-language analytics opportunities such as text-to-SQL, chat-based BI, semantic search, RAG-enabled insight assistants and conversational workspaces, with appropriate governance and access controls.
- AI and GenAI solutioning: Lead discovery and design for ML, GenAI and agentic use cases; assess feasibility and value; define target architecture; and establish evaluation, monitoring and Responsible AI guardrails.
- Customer workshops: Plan and facilitate Data and AI discovery sessions, current-state assessments, architecture workshops and use-case prioritization discussions, converting outcomes into a sequenced roadmap and qualified pipeline.
- Proposals and presales: Own or contribute to RFP, RFI and proactive proposals, including solution approach, reference architecture, estimates, delivery model, staffing and pricing inputs; present and defend recommendations to business and technical stakeholders.
- Offerings and assets: Convert recurring customer needs into reusable accelerators, PoVs, demos, reference architectures and playbooks with Data and AI CoEs and alliance partners.
- Delivery governance: Guide programs from assessment and PoC through pilot and production; review key architecture and engineering deliverables; and ensure quality, security, observability, cost control and business outcomes.
- Capability building: Define skill priorities across Data Engineering, Data Management, analytics and AI; mentor consultants, architects and engineers; and maintain a current market and technology point of view.
Generic Managerial Skills, If any
- Ability to lead a technical capability without formal line authority - setting direction, standards and priorities across pods and accounts.
- Strong consultative and communication skills - credible with principal AI engineers and confident presenting to executive stakeholders; willing and able to travel regularly to customer locations across the US West Coast.
- Experience coordinating across geographies and time zones with offshore delivery and CoE teams; mentoring and growing technical talent.
- Comfortable in an early-stage unit - self-directed, pragmatic and able to build structure where none yet exists.
Key Words to search in Resume
Data and AI Consultant; Data & AI Advisory; Data Strategy; AI Strategy; Data Engineering; Python; SQL; Apache Spark; PySpark; ETL / ELT; Data Pipelines; Kafka; Data Lakehouse; Data Warehouse; Medallion Architecture; Data Mesh; Data Management; Master Data Management; Metadata Management; Data Catalog; Data Quality; Data Lineage; Data Governance; Data Observability; Conversational Analytics; Conversational BI; Text-to-SQL; Semantic Layer; Enterprise Search; Generative AI; Agentic AI; RAG; Vector Databases; LangChain; LangGraph; Semantic Kernel; LLM Evaluation; Responsible AI; MLOps / LLMOps; Databricks; Snowflake; Microsoft Fabric; Azure OpenAI; Azure ML; Google BigQuery; Vertex AI; AWS SageMaker; Bedrock; RFP / RFI; Presales Solutioning; Discovery Workshops; Data and AI CoE; PoC to Production; High-Tech Domain
Pre-Screening Questionnaire
Describe a Data and AI transformation or solution you shaped from discovery through production. What business problem, data architecture, AI components and outcomes were involved?
What is your working experience with modern Data Engineering, including cloud data platforms, Spark / PySpark, SQL, ETL / ELT, batch or streaming pipelines?
How have you addressed Data Management areas such as data quality, metadata, lineage, Master Data Management, governance, security and privacy?
Describe a conversational analytics solution you designed or advised on. How did you address semantic context, text-to-SQL or RAG, data access, accuracy and user adoption?
What is your hands-on experience with GenAI, RAG, agentic frameworks and LLM evaluation? Which platforms and frameworks have you used?
Tell us about a customer discovery or solution workshop you facilitated and how it translated into a roadmap, proposal or delivery program.
Describe an RFP, RFI or proactive Data and AI proposal you owned, including your role in architecture, estimation and presentation.
Have you built or led a Data and AI practice, CoE or capability pod? How did you define offerings, assets, standards and skills?
What is your experience with high-technology customers and distributed onsite-offshore delivery teams?
Are you Bay Area based or willing to relocate and travel regularly? What is your work authorization status?
Salary Range: $180,000 - $220,000 a year
#LI-CM2
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.