Agentic AI Architect

Job Title: Agentic AI Architect

Role Summary

The Agentic AI Architect is responsible for designing, building, and operationalizing autonomous AI systems (AI agents) that can reason, plan, and execute tasks across enterprise ecosystems. This role combines deep expertise in AI/ML, Generative AI, distributed systems, and enterprise architecture to enable intelligent, self-orchestrating workflows that enhance business productivity and decision-making.

Key Responsibilities

1. Architecture & Design

Define end-to-end Agentic AI architecture frameworks leveraging LLMs, multi-agent systems, and orchestration layers

Design autonomous AI agents capable of planning, reasoning, memory management, and tool usage

Establish reference architectures for enterprise-grade AI applications (cloud-native, scalable, secure)

Integrate AI agents with enterprise systems (Data Platforms, ERP, CRM, APIs, and event-driven systems)

2. Agentic AI Development

Build and deploy multi-agent systems using frameworks such as LangChain, Semantic Kernel, AutoGen, CrewAI, or similar

Implement agent orchestration patterns (planner-executor, reflection loops, self-healing agents)

Develop agents with capabilities including:
Task decomposition and planning

Contextual reasoning and chaining

Memory (short-term, long-term, vector-based retrieval)

Tool calling and API integrations

3. GenAI & LLM Integration

Architect solutions leveraging leading LLMs (Azure OpenAI, OpenAI, Anthropic, etc.)

Implement RAG (Retrieval-Augmented Generation) pipelines with vector databases (FAISS, Pinecone, Azure AI Search, etc.)

Optimize prompt engineering, fine-tuning, and grounding strategies

Ensure efficient token usage, latency, and cost optimization

4. Enterprise Integration

Integrate Agentic AI solutions with:
Data ecosystems (Azure Databricks, Synapse, Snowflake)

Workflow tools (ServiceNow, Power Platform, custom enterprise apps)

APIs, microservices, and event-driven architectures

Enable AI-driven automation across business processes

5. Governance, Security & Responsible AI

Define AI governance frameworks (auditability, compliance, explainability)

Implement guardrails for safe and responsible AI usage

Ensure data privacy, model security, and regulatory compliance

Design monitoring mechanisms for hallucination detection and agent reliability

6. Performance Optimization & Scalability

Optimize inference performance, caching strategies, and execution flows

Design scalable multi-agent systems across distributed/cloud environments

Monitor throughput, reliability, and system health

7. Leadership & Strategy

Lead architecture discussions with stakeholders and executive leadership

Drive AI adoption strategy and roadmap across business units

Mentor engineering teams on Agentic AI best practices

Evaluate emerging tools, frameworks, and innovations in AI ecosystems

Required Qualifications

Education

Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or related field

Experience

10+ years in software engineering, data engineering, or enterprise architecture

3–5+ years in AI/ML or Generative AI solution development

Hands-on experience with LLM-based applications and orchestration frameworks

Required Technical Skills

AI/ML & GenAI

LLMs, prompt engineering, fine-tuning

RAG, embeddings, vector databases

Multi-agent architectures and agent frameworks

Programming

Python (primary), with experience in AI libraries

Familiarity with Java/Scala/Node.js (optional but useful)

Cloud Platforms

Azure (preferred), AWS, or GCP

Azure OpenAI, Azure AI Studio, Databricks

Data & Platforms

Data engineering ecosystems (Databricks, Snowflake, Synapse)

REST APIs, microservices, event streaming (Kafka, Event Hub)

DevOps & MLOps

CI/CD pipelines, Docker, Kubernetes

Monitoring, logging, experiment tracking

Preferred Skills

Experience with Autonomous AI systems / AI agents in production

Knowledge of knowledge graphs and semantic search

Exposure to reinforcement learning or adaptive systems

Experience in Insurance/Financial Services domain (nice to have for enterprise roles like AIG)

Key Competencies

Strategic thinking and enterprise architecture design

Strong problem-solving and system design skills

Excellent stakeholder communication and leadership abilities

Ability to translate business requirements into AI solutions

• Please note this role is not able to offer visa transfer or sponsorship now or in the future*

We're excited to meet people who share our mission and who can make an impact in a variety of ways. Don't hesitate to apply—even if you only meet the minimum requirements. Think about your transferable experiences and unique skills that make you stand out.

Salary and Other Compensation:
Applications will be accepted until July 29, 2026,

The annual salary for this position is between $ 90,000 - $ 150,000 depending on experience and other qualifications of the successful candidate.

This position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans.

Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:

Medical/Dental/Vision/Life Insurance

Paid holidays plus Paid Time Off

401(k) plan and contributions

Long-term/Short-term Disability

Paid Parental Leave

Employee Stock Purchase Plan

Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.