GenAI Solutions Architect

Infosys; Irving (TX); On-site; USD 150,000 - 200,000; Full time

Job summary

Infosys is seeking a senior GenAI/ML Engineer to architect and deliver production-grade AI solutions for enterprise clients. You will lead discovery workshops, design architectures, and build end-to-end GenAI features with strong emphasis on data quality, security, and ROI.

You will collaborate with cross-functional teams to deploy scalable AI systems, evaluate models, and ensure responsible AI practices throughout the lifecycle.

Qualifications

• 6-12 years in software development, including 2+ years delivering ML/GenAI or data solutions.

Responsibilities

• Advise clients on GenAI and agentic AI use cases, discovery workshops, and risk assessments.
• Build business cases with success metrics and value tracking.
• Evaluate GenAI models and design solution architecture for client problems.
• Design and prototype GenAI features focused on user value.
• Define evaluation methods using golden datasets and metrics.
• Optimize models for performance and cost, deploy AI solutions.
• Set production-readiness criteria with monitoring for drift.
• Design IDP solutions with OCR and extraction in GenAI/RAG.
• Mentor team members on GenAI design and evaluation.

Skills

ML/GenAI experience

Python proficiency

LLM evaluation design

Model quality/latency trade-offs

Responsible AI & GenAI security

Agent frameworks & tool-calling

Cloud AI platforms

Data structures & algorithms

Client-facing experience

Prototyping to production

Education

Bachelor's or Master's in CS/DS/Engineering

Tools

LangChain

LangGraph

Google ADK

CrewAI

AutoGen

AWS Bedrock

Vertex AI

Azure OpenAI

Job description

Infosys is seeking a senior GenAI/ML Engineer to architect and deliver production-grade AI solutions for enterprise clients. You will lead discovery workshops, design architectures, and build end-to-end GenAI features with strong emphasis on data quality, security, and ROI.

You will collaborate with cross-functional teams to deploy scalable AI systems, evaluate models, and ensure responsible AI practices throughout the lifecycle.