Senior AI Engineer

Infosys•Mason, OH

About The Position

Infosys is a global leader in next-generation digital services and consulting, enabling clients in over 50 countries to navigate their digital transformation. With over four decades of experience, Infosys expertly guides clients through their digital journey by enabling the enterprise with an AI-powered core. They empower businesses with agile digital at scale to deliver unprecedented levels of performance and customer delight. Their always-on learning agenda drives continuous improvement through building and transferring digital skills, expertise, and ideas from their innovation ecosystem. The Infosys Engineering unit is dedicated to amplifying product-centric value delivery through innovative engineering solutions. They focus on adopting a data-first approach to engineering and value-stream management, enabling real-time visibility into the flow of value. The team leverages advanced technologies such as AI, ML, and predictive analytics to drive continuous improvement, enhance delivery processes, and accelerate time to market. This role is part of a team at the forefront of engineering innovation, delivering intelligent insights and empowering stakeholders to achieve hyper-productivity and sustainable growth.

Requirements

Senior AI engineer to design and build production-grade LLM-powered applications and agentic systems.
This role owns the end-to-end development of intelligent solutions from architecture to deployment - leveraging Python, modern LLM frameworks, and scalable system design.
Strong Python expertise with experience building and deploying production-grade backend systems.
Hands-on experience developing applications using LLMs, including prompt engineering and orchestration.
Proven experience with RAG architectures, embeddings, and vector databases.
Experience with agentic frameworks (e.g., LangChain, LangGraph, AutoGen).
Strong system design skills with experience building and scaling cloud-based applications.
Bachelor’s degree or foreign equivalent required from an accredited institution. Will also consider three years of progressive experience in the specialty in lieu of every year of education.
Candidates authorized to work for any employer in the United States without employer-based visa sponsorship are welcome to apply.

Nice To Haves

The ability to analyse, design, and develop scalable and secure architectures.
A knack for assessing multiple technologies, tools and frameworks to recommend optimal solutions.
Understanding of integration methodologies, proof-of-concept validation and deployment strategies.
The ability to document architectural frameworks and solutions.
Proactive communication with internal teams and clients, ensuring alignment with business objectives.
A knack to keep with up technical advancements to ensure relevant and effective recommendations.
The ability to maintain standards and ensure smooth transition through knowledge transfer.

Responsibilities

Participate in customer discussions to gather and analyze system requirements for performance, security, and scalability.
Provide suggestions on solution architecture, contribute to tech framework development, support enterprise architecture definition, and drive design creation.
Identify suitable technologies and tools available in the market, and perform a comparative analysis in line with client's business requirements, thereby identifying optimal architectural solution options.
Participate in developing integration strategies based on validation of proof of concept of proposed architectural solutions.
Facilitate a smooth transition by ensuring knowledge transfer and adherence to quality standards, while contributing to the build and deployment automation strategy.
Collaborate with development teams to resolve complex technical issues using solution deployment and roll-out methods, during build, deployment, and support phases.
Analyze system performance basis architectural solutions implemented and support in identifying potential risks in the system landscape.
Create artifacts of architectural frameworks and technical solutions, thereby enriching the team's knowledge repository.
Architect and deliver end-to-end LLM-powered applications and agentic workflows using Python.
Design and implement RAG pipelines over enterprise data using embeddings and vector databases.
Build multi-step, tool-using agents (planning, execution, memory) using frameworks such as LangChain.
Integrate AI systems with APIs, backend services, and cloud platforms.
Establish evaluation, reliability, and performance strategies (accuracy, latency, cost).