ST-L1

Infosys

About The Position

The Senior Technologist role is part of Infosys’ Strategic Technology Group (STG), a unit for experienced technologists who work on complex, large-scale technology challenges. STG members contribute to technology direction, solve business challenges, deliver resilient and high-quality outcomes, mentor teams, and influence best practices across programs. The role applies technical expertise to complex software and AI initiatives. It involves evaluating requirements, designing frameworks and reusable assets, producing production-ready code, supporting technical decisions and deployments, and contributing to quality and service benchmarks. The Senior Technologist also explores emerging technologies, develops proof of concepts, and collaborates with peers and stakeholders to deliver project outcomes. The position requires core development experience and knowledge of analysis and design, along with practical exposure to Agentic AI, RAG pipelines, MCP, machine learning, deep learning, NLP, Python, LangChain, AI cloud services, and TensorFlow or PyTorch. The role also involves responsible AI practices, code review, testing, collaboration, and ongoing learning. A bachelor’s degree or foreign equivalent is required, with consideration of progressive specialty experience in lieu of education. Relocation and/or travel to the work or project location may be required. Applicants must be authorized to work in the United States without employer-based visa sponsorship; Infosys cannot provide sponsorship for this role.

Requirements

Experience in core development, with working knowledge of analysis and design.
Ability to contribute to design and architecture discussions and apply established design and architecture patterns in assigned modules.
Hands-on exposure to implementing Agentic AI, building RAG pipelines, and MCP through project work or prior roles.
Good understanding of machine learning fundamentals, deep learning architectures, and natural language processing.
Proficiency in Python and LangChain, and working experience with AI cloud services such as AWS Bedrock or Azure OpenAI and TensorFlow or PyTorch.
Ability to assist with fine-tuning pretrained LLMs for specific tasks and domains, with guidance from senior team members.
Ability to take ownership of individual AI development lifecycle stages and understand how they connect end to end.
Ability to collaborate with data scientists, software engineers, and product managers, understand project requirements, and flag technical feasibility concerns early.
Interest in generative AI research and willingness to share learnings with the team.
Awareness of responsible AI practices, including bias mitigation, fairness, and traceability.
Working experience with open-source frameworks and tools.
Ability to write clean, well-tested, and performant code.
Ability to review code performance, respond to feedback, and participate in code reviews.
Comfort working in a fast-paced, agile environment.
Good collaboration and communication skills.
Bachelor’s degree or foreign equivalent from an accredited institution; three years of progressive experience in the specialty may be considered in lieu of each year of education.
Must be authorized to work for any employer in the United States without employer-based visa sponsorship; Infosys is unable to provide immigration sponsorship for this role.

Nice To Haves

Master’s degree in computer science, Artificial Intelligence, or a related field, or equivalent experience.
Experience with AI cloud-based platforms such as AWS, GCP, or Azure.
Experience fine-tuning open-source AI models.
Strong understanding of one or more domains and associated industry standards.
Industry-acclaimed external certifications, if applicable.
Active participation in technology forums.
Contributions to the open-source community.

Responsibilities

Evaluate project requirements and use cases, and deliver high-quality designs, frameworks, and production-ready code.
Design technology frameworks for complex scenarios, develop design patterns and reference implementations, create reusable assets, and conduct design reviews.
Deliver high-quality code across applications, databases, and integrations.
Optimize code, debug complex routines, and develop code generators and pilot implementations for reusable components.
Work with other teams to align on project goals and technology implementations.
Provide technical guidance to peers and help maintain delivery consistency and code quality.
Identify and independently develop proof of concepts for emerging programming technologies and complex modules.
Provide input for high-risk, multi-technology and multi-language deployments.
Evaluate and implement technical services that meet quality-of-service requirements and align with business and IT KPIs.
Deliver reusable assets and defect-free code in support of quality benchmarks and Zero-Defect Delivery.
Resolve technical challenges on complex projects and reduce risk exposure.
Monitor emerging technologies and industry trends to inform decisions and improve project outcomes.
Support test automation and execute test strategies to improve quality.
Apply expertise in designing scalable architectures and frameworks for complex technology challenges.
Mentor peers, share knowledge, and foster technical excellence across projects.
Assess emerging technologies and translate them into proof-of-concepts and deployment-ready solutions.
Drive performance improvements through engineering practices and service benchmarks.
Anticipate risks and resolve technical challenges to support resilience and project success.
Contribute to design and architecture discussions and apply established design and architecture patterns in assigned modules.
Implement Agentic AI, RAG pipelines, and MCP through project work or prior roles.
Assist with fine-tuning pretrained LLMs for specific tasks and domains, with guidance from senior team members.
Own defined stages of the AI development lifecycle, such as data preprocessing, model training, evaluation, optimization, or deployment.
Collaborate with data scientists, software engineers, and product managers to clarify requirements and raise feasibility concerns early.
Follow and promote responsible AI practices, including awareness of bias mitigation, fairness, and traceability.
Write clean, well-tested, performant code; review code performance and act on feedback.
Participate in code reviews as both a reviewer and recipient of feedback.
Support proof-of-concepts for new technologies and take responsibility for defined parts of the work.