GenAI Testing Consultant/Architect

Infosys; Bengaluru; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Infosys is seeking an experienced cloud AI architect in Bengaluru to lead GenAI initiatives with scalable architectures and cost‑effective deployments.

You will design end‑to‑end GenAI systems, govern architectures, and collaborate across teams to deliver robust, maintainable AI platforms using AWS/Azure/GCP.

Strong ML/LLMOps, Python skills, and data‑engineering know‑how are essential for success.

Qualifications

• Experience architecting Cloud AI solutions with scalable inference and cost/performance optimization.
• Hands-on with RAG, tool/function calling, prompt engineering, and automated evaluation frameworks.
• Strong MLOps/LLMOps: experiment tracking, model registry, CI/CD, observability, drift detection, and incident response.
• Experience with data engineering concepts (feature stores, batch/stream processing) to support ML/GenAI workloads.
• Proven ability to lead architecture governance and drive adoption across teams and portfolios.

Responsibilities

• Architect end-to-end Generative AI systems including data pipelines, model selection, orchestration, evaluation, and deployment patterns.
• Lead discovery workshops to identify high-value GenAI use cases and create solution roadmaps.
• Collaborate with cloud and platform teams to design scalable Cloud AI architectures on AWS, Azure, or GCP.
• Test and validate GenAI applications, capture quality metrics, and drive continuous improvement.

Skills

Cloud AI architecture

LLM app patterns

MLOps/LLMOps

Data engineering

Architecture governance

GenAI solution design

Python programming

Tool calling

Workflow orchestration

Education

Bachelor's or Master’s in CS/AI/SE

Tools

AWS

Azure

GCP

CI/CD pipelines

Model registry

Feature stores

Job description

• Experience architecting Cloud AI solutions, including scalable inference, data security patterns, and cost/performance optimization.
• Hands‑on experience with LLM application patterns such as RAG, tool/function calling, prompt engineering, and automated evaluation frameworks.
• Strong MLOps/LLMOps expertise: experiment tracking, model registry, CI/CD, observability, drift detection, and incident response for AI services.
• Experience with data engineering concepts (feature stores, batch/stream processing) to support ML and GenAI workloads.
• Proven ability to lead architecture governance, create reference architectures, and drive adoption across teams and portfolios.
• Lead discovery workshops to identify high‑value GenAI and ML use cases, define success metrics (standard and custom), and create solution roadmaps.
• Architect end-to-end Generative AI systems, including data pipelines, model selection, orchestration, evaluation, and deployment patterns using Infosys and industry‑standard tools and platforms.
• Design and implement ML/AI solutions using Python (or other relevant Data Science/AI languages), applying strong software engineering practices for maintainability, performance, and reliability.
• Build MCP and agentic‑based solutions using appropriate frameworks, enabling tool/function calling, workflow orchestration, and multi‑step reasoning patterns aligned to business needs.
• Test and validate GenAI applications using relevant tools and frameworks; capture, analyze, and report standard and custom quality metrics (e.g., relevance, groundedness, toxicity, latency, cost) and drive continuous improvement.
• Collaborate with cloud and platform teams to design scalable Cloud AI architectures and cost‑efficient deployments across AWS, Azure, or GCP.
• Explore and evaluate new technologies, tools, and testing methodologies to improve development processes and solution quality; stay up‑to‑date with advancements in Generative AI and evaluation practices.

• Experience architecting Cloud AI solutions, including scalable inference, data security patterns, and cost/performance optimization.
• Hands‑on experience with LLM application patterns such as RAG, tool/function calling, prompt engineering, and automated evaluation frameworks.
• Strong MLOps/LLMOps expertise: experiment tracking, model registry, CI/CD, observability, drift detection, and incident response for AI services.
• Experience with data engineering concepts (feature stores, batch/stream processing) to support ML and GenAI workloads.
• Proven ability to lead architecture governance, create reference architectures, and drive adoption across teams and portfolios.
• Lead discovery workshops to identify high‑value GenAI and ML use cases, define success metrics (standard and custom), and create solution roadmaps.
• Architect end-to-end Generative AI systems, including data pipelines, model selection, orchestration, evaluation, and deployment patterns using Infosys and industry‑standard tools and platforms.
• Design and implement ML/AI solutions using Python (or other relevant Data Science/AI languages), applying strong software engineering practices for maintainability, performance, and reliability.
• Build MCP and agentic‑based solutions using appropriate frameworks, enabling tool/function calling, workflow orchestration, and multi‑step reasoning patterns aligned to business needs.
• Test and validate GenAI applications using relevant tools and frameworks; capture, analyze, and report standard and custom quality metrics (e.g., relevance, groundedness, toxicity, latency, cost) and drive continuous improvement.
• Collaborate with cloud and platform teams to design scalable Cloud AI architectures and cost‑efficient deployments across AWS, Azure, or GCP.
• Explore and evaluate new technologies, tools, and testing methodologies to improve development processes and solution quality; stay up‑to‑date with advancements in Generative AI and evaluation practices.

Good to have skills:GenAI, Python, Agile methodologies, CI/CD pipelines, API testing, SQL/Database basics Location: BANGALORE, PUNE, HYDERABAD, CHENNAI, TVM, INDORE, NAGPUR, MLR, MYS, NOIDA, BBSR, KOLKATA, COIMBATORE, HUBLI