Agentic AI Engineer

Infosys

22h

About The Position

The Infosys Financial Services unit is a global leader in driving digital transformation for financial institutions. We specialize in leveraging advanced technologies such as AI, cloud, and data-led innovation to help our clients accelerate growth and unlock business value. Our AI-driven solutions empower financial institutions to make smarter decisions, enhance customer experiences, and achieve operational excellence. Join us to be part of a pioneering team that is at the forefront of financial services transformation. You'll have the opportunity to work with cutting-edge AI technologies, collaborate with industry experts, and contribute to transformative projects across banking, wealth management, and insurance sectors. If you're passionate about AI and eager to make a significant impact, the Infosys Financial Services unit is the perfect place for you to grow and excel.

Requirements

OpenAI Codex, AI Coding Agents, GitHub Copilot Enterprise administration and deployment.
AI Agent Engineering, orchestration, Custom GPTs, Assistants, and MCP (Model Context Protocol).
MCP security, authorization, enterprise IAM, RBAC, and fine-grained access controls.
SSO, SAML, OAuth 2.0, OIDC, SCIM Provisioning, Active Directory, and Microsoft Entra ID.
AWS IAM, AWS Identity Center, Bedrock, Bedrock Agents, ECS, Lambda, API Gateway, CloudWatch, and VPC.
Strong Python development, Async Python, REST APIs, and automation/integration services.
Structured logging, test automation, regression testing, and agent evaluation frameworks.
Bash/Shell scripting and PowerShell.
Prompt injection testing, AI security validation, and enterprise AI platform operations.
Technical leadership, cross-functional collaboration, and end-to-end SDLC delivery.
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.
This position may require relocation and/or travel to work/project location.
All applicants authorized to work in the United States are encouraged to apply.

Nice To Haves

Enterprise-level production experience with AI coding platforms and AI-assisted development tools.
Hands-on experience implementing least-privilege access controls for AI agents and autonomous systems.
Strong understanding of Model Context Protocol (MCP) security boundaries and agent security architecture.
Advanced proficiency in Python, including asynchronous programming (Async Python).
Experience developing and maintaining APIs using modern API frameworks such as FastAPI, Flask, or similar.
Expertise in structured logging, observability, and monitoring for distributed applications.
Experience building automated agent evaluation frameworks and AI application validation processes.
Strong background in test harness development, automated testing, and regression testing suites.
Proficiency in Bash/Shell scripting and PowerShell for automation and operational tasks.
Experience conducting prompt injection testing, AI security assessments, and vulnerability analysis.
Demonstrated technical leadership with experience mentoring teams, driving technical decisions, and leading solution delivery.

Responsibilities

Develop data preparation tasks, while identifying patterns or anomalies.
Ensure data readiness for advanced modeling.
Develop models for complex use cases (e.g., forecasting models, LLM-based solutions), while refining algorithms to meet business needs, and ensure smooth deployment into scalable, production-ready solutions.
Conduct testing and optimize algorithms for performance, reliability, and scalability, while providing guidance to team members in best practices.
Design and develop predictive models and data-driven analyses to address business challenges.
Build, evaluate, and deploy models, standardize code, and contribute to knowledge management.
Leverage tools like SAS and R/Python to create reusable customizations for non-ML, ML, and deep learning algorithms, while enhancing analytics including LLMs, and create innovative, cost-effective solutions.
Define analytics problems for projects; execute visualization, analysis, and predictive modeling under guidance.
Proactively maintain models and implement improvements for accuracy and reliability.
Apply governance controls to mitigate risks and ensure compliance.
Analyze performance trends, recommend improvements, and document discrepancies for escalation.
Maintain comprehensive documentation standards, while participating in knowledge transfer sessions.
Participate in discussions with stakeholders to refine requirements, provide insights, and guide implementation of models.
Apply the predefined quality measurement framework at an individual task level in the project.
Deploy complex analytics tools or multi-system integration, while validating deployment success.
Participate in developing scripts or templates for repeated deployments tasks.
Contribute to analytic solutions, IP asset creation, and training initiatives.
Contribute to thought leadership such as papers, innovative non-ML, ML, deep learning or LLM models, and proofs of concept.
Participate in and deliver analytics training, while contributing to content creation.
Provide input for segment and unit-level business plans.
Deliver scalable, high-quality analytics solutions aligned to business needs.
Drive innovation through advanced analytics, automation and thought leadership.
Enable team growth through knowledge sharing, training and standardization.
Support business planning with data-driven insights.