AI Senior Engineer

Tata Consultancy Services; Waco (TX); On-site; USD 100,000 - 120,000; Full time

Benefits offered by this job

Discretionary Annual Incentive

Comprehensive Medical Coverage

Maternal & Parental Leaves

Auto & Home Insurance

Certification & Training Reimbursement

Vacation & Holidays

401K Plan

Job summary

Tata Consultancy Services in Texas is seeking a seasoned AI solutions lead to design and implement enterprise-grade agentic AI systems. You will build data-driven workflows, integrate AI agents with Fabric Lakehouse/ Warehouse, semantic models, APIs, and enterprise systems to automate processes.

Responsibilities include developing RAG pipelines using embeddings and vector search, translating business needs into ontology models, and ensuring guardrails, security, and cost optimization across

Qualifications

• Agentic AI with agents, planning, memory, tool calling, and multi-agent orchestration.
• Guardrails and agent evaluation requirements.
• Frameworks: LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or equivalent frameworks.
• Orchestration frameworks.
• LLM/GenAI with Azure OpenAI, embeddings, vector search, and RAG.
• Microsoft Fabric components and Power BI semantic models.
• Ontology/semantic modeling including metadata and knowledge graphs.
• Engineering: Python, SQL, Spark, REST APIs, CI/CD, Git.
• Cloud security and production support.
• Translate business processes into ontology models and semantic data products.

Responsibilities

• Design and implement enterprise-grade agentic AI solutions that retrieve information, call tools/APIs, reason over data, and automate workflows.
• Integrate AI agents with Fabric Lakehouse/Warehouse, semantic models, APIs, and enterprise systems.
• Build and optimize RAG pipelines using embeddings, vector/semantic search, and data grounding.
• Design ontology-driven semantic models covering entities, relationships, metadata, governance.
• Develop Fabric data pipelines using Data Factory, Notebooks, Spark/PySpark, SQL, Delta Lake, and medallion architecture.
• Implement AI guardrails, access control, logging, monitoring, cost optimization, and trustworthy AI practices.
• Create technical designs, architecture diagrams, reusable components, and coding standards.

Skills

Agentic AI

Guardrails

Agent evaluation

Prompt engineering

Grounding

Semantic modeling

RAG pipelines

Tools

LangChain

LangGraph

Semantic Kernel

AutoGen

CrewAI

Azure OpenAI

Power BI

Delta Lake

Data Factory

Notebooks

Spark

Python

SQL

Job description

• Agentic AI: AI agents, planning, reasoning, memory, tool/function calling, multi-agent orchestration,
• guardrails, and agent evaluation.
• Frameworks: LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or equivalent agent
• orchestration frameworks.
• LLM / GenAI: Azure OpenAI/OpenAI, RAG, embeddings, vector search, hybrid search,
• prompt engineering, grounding, and hallucination mitigation.
• Microsoft Fabric: Lakehouse, Warehouse, OneLake, Data Factory, Dataflows, Notebooks,
• Power BI semantic models, Direct Lake, Delta Lake, and medallion architecture.
• Ontology / Semantic Modeling: business entities, relationships, hierarchies, taxonomy,
• metadata, lineage, semantic layer, knowledge graphs, RDF/OWL/SPARQL or graph databases.
• cloud security, and production support.
• Functional: ability to translate business processes into ontology models, semantic data products, and agent workflows
• Design and implement enterprise-grade agentic AI solutions that retrieve information,
• call tools/APIs, reason over data, and automate workflows.
• Integrate AI agents with Microsoft Fabric Lakehouse/Warehouse, semantic models,
• APIs, enterprise systems, and document repositories.
• Build and optimize RAG pipelines using embeddings, vector/semantic search, structured
• data grounding, and evaluation datasets.
• Design ontology-driven semantic models covering entities, relationships, metadata,
• business rules, lineage, and governance.
• Delta Lake, and medallion architecture.
• Implement AI guardrails, access control, logging, monitoring, cost optimization, and
• responsible AI practices.

Job Description

Must Have Technical/Functional Skills

• Agentic AI: AI agents, planning, reasoning, memory, tool/function calling, multi-agent orchestration,
• guardrails, and agent evaluation.
• Frameworks: LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or equivalent agent
• orchestration frameworks.
• LLM / GenAI: Azure OpenAI/OpenAI, RAG, embeddings, vector search, hybrid search,
• prompt engineering, grounding, and hallucination mitigation.
• Microsoft Fabric: Lakehouse, Warehouse, OneLake, Data Factory, Dataflows, Notebooks,
• Power BI semantic models, Direct Lake, Delta Lake, and medallion architecture.
• Ontology / Semantic Modeling: business entities, relationships, hierarchies, taxonomy,
• metadata, lineage, semantic layer, knowledge graphs, RDF/OWL/SPARQL or graph databases.
• Engineering: Python, SQL, Spark/PySpark, REST APIs, CI/CD, Git, monitoring, logging,
• cloud security, and production support.
• Functional: ability to translate business processes into ontology models, semantic data products, and agent workflows

Roles & Responsibilities

• Design and implement enterprise-grade agentic AI solutions that retrieve information,
• call tools/APIs, reason over data, and automate workflows.
• Integrate AI agents with Microsoft Fabric Lakehouse/Warehouse, semantic models,
• APIs, enterprise systems, and document repositories.
• Build and optimize RAG pipelines using embeddings, vector/semantic search, structured
• data grounding, and evaluation datasets.
• Design ontology-driven semantic models covering entities, relationships, metadata,
• business rules, lineage, and governance.
• Develop Fabric data pipelines using Data Factory, Notebooks, Spark/PySpark, SQL,
• Delta Lake, and medallion architecture.
• Implement AI guardrails, access control, logging, monitoring, cost optimization, and
• responsible AI practices.
• Create technical designs, architecture diagrams, reusable components, coding standards etc.

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-$100,000-$120,000 a year