AI Architect
Indexed description
In the assigned Job Role of Data Science Consultant 3, your Area Of Responsibility will be as below:
- Review data preparation tasks, and plans to address patterns or anomalies, while ensuring data readiness for advanced modeling and AI.
- Review models for complex use cases (e.g., forecasting models, LLM-based solutions), and refine algorithms to meet business needs.
- Review plan for smooth deployment into scalable, production-ready solutions.
- Review test plans and test results for analytics use cases, while defining optimization standards for model accuracy and stability, in alignment with business goals.
- Build models and analytics solutions tailored to business needs.
- Ensure quality and scalability across client engagements while actively contributing to knowledge assets and innovation streams.
- 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.
- Review and refine analytics problems; identify data sources and extract from diverse environments.
- Oversee analysis execution and drive business insights.
- Create monitoring strategies across multiple projects, embedding governance frameworks to ensure robustness, reliability, and risk awareness.
- Review monitoring frameworks, refine documentation/reporting templates, and present insights on anomalies or slippages to stakeholders.
- Refine documentation strategy across teams, ensuring transparency and reproducibility of complex analytics solutions.
- Collaborate with cross-functional teams, ensuring alignment between analytics delivery and business strategy.
- Review analytics outputs for adherence to quality frameworks and project commitments.
- Recommend improvements to quality metrics and guide team members to align with standards.
- Identify and recommend model changes needed for successful deployment.
- Engage in creation and refinement of IP assets such as analytics prototypes and accelerators.
- Develop insights, whitepapers, and proof-of-concept summaries that highlight innovative thinking.
- Review innovative models and applications in non-ML, ML, deep learning, or LLM areas.
- Support participation in forums and internal knowledge exchanges.
- Deliver training sessions on technical and analytics-specific topics.
- Collaborate on content creation and mentor team members through hands- on guidance in live projects.
- Provide input for segment and unit-level business plans.
- A strong focus on innovation and scalable analytics solutions.
- Proactive problem-solving ability for complex, data-driven business challenges.
- Deep technical expertise across advanced modeling and AI use cases.
- A strategic mindset to align analytics with business goals.
- Ability to mentor team members and drive continuous improvement.
- Strong communication and knowledge-sharing capabilities.
- Architect and implement production-grade AI agent solutions on Google Cloud Platform (GCP), with Azure as a supporting cloud environment where required.
- Design end-to-end AI systems including:
- Agent orchestration
- Short-term memory
- Long-term memory
- Context management
- Tool integrations
- Workflow execution
- Evaluation pipelines
- Build and standardize architecture for production-ready AI agents, ensuring scalability, resilience, security, and maintainability.
- Define and implement AI Gateway patterns for model access, routing, authentication, rate limiting, and policy enforcement.
- Design and deploy guardrails for responsible AI, safety, compliance, prompt protection, hallucination mitigation, and output validation.
- Establish tracing and observability frameworks for AI applications using GCP-native monitoring capabilities and Dynatrace.
- Implement evaluation services for AI systems, including:
- Offline evaluation
- Online evaluation
- Model and agent performance benchmarking
- Quality and reliability measurement
- Define and implement architecture for memory systems, including short-term conversational memory and persistent long-term memory.
- Collaborate with engineering, data, product, security, and customer stakeholders to align AI solutions with business and technical requirements.
- Lead technical discussions with different customer stakeholders, translating business needs into scalable AI architectures.
- Drive architecture decisions under aggressive timelines while maintaining delivery quality and engineering rigor.
- Ensure AI systems adhere to enterprise standards for governance, privacy, security, compliance, and operational excellence.
- Provide technical leadership, mentorship, and architecture guidance to cross-functional teams.
Additional Required Qualifications
- 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.
- Candidates authorized to work for any employer in the United States without employer-based visa sponsorship are welcome to apply. Infosys is unable to provide immigration sponsorship for this role now or in the future.
- Medical/Dental/Vision/Life Insurance
- Long-term/Short-term Disability
- Health and Dependent Care Reimbursement Accounts
- Insurance (Accident, Critical Illness , Hospital Indemnity, Legal)
- 401(k) plan and contributions dependent on salary level
- Paid holidays plus Paid Time Off
EEO
Infosys provides equal employment opportunities to applicants and employees without regard to race; color; sex; gender identity; sexual orientation; religious practices and observances; national origin; pregnancy, childbirth, or related medical conditions; status as a protected veteran or spouse/family member of a protected veteran; or disability.
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