Senior AI Architect to lead

VIRTUSA•Hartford, CT

45d

About The Position

Role OverviewWe are seeking a Senior AI Architect to lead the design development and governance of cutting edge AI solutions This role requires both hands on technical expertise and strategic leadershi driving innovation while ensuring compliance security and scalability across our AI ecosystemKey ResponsibilitiesAI Leadership StrategyLead our end to end AI development balancing hands on model design and implementation with guiding and mentoring the broader AI engineering and research teamsArchitecture Design and DeploymentDesign build and deploy production grade machine learning models including LLMs on the Google Cloud Platform Vertex AI using Python and related frameworksCollaboration Ecosystem ManagementPartner closely with internal AI teams Google and other technology vendors to sustain momentum drive innovation and align with evolving best practicesGovernance Security and AIRB ComplianceOwn and execute AI governance processes including AI Risk Board AIRB reviews data security compliance and ethical AI considerations across all AI initiativesModel Development EvaluationDesign and implement models for Conversational AI leveraging Google CCAI CES components Lead model evaluation using robust Eval frameworks and metrics to ensure reliability accuracy and fairnessAdvanced ArchitecturesApply agentic architectures and Retrieval Augmented Generation RAG to build intelligent context aware AI systems Experience with Parameter Efficient Fine Tuning PEFT is a plusPerformance ScalabilityOptimize and fine tune models for performance efficiency scalability and accuracy across production workloadsCross Functional CollaborationWork with business stakeholders and engineering teams to translate complex business problems into scalable and efficient AI driven solutionsContinuous InnovationStay ahead of emerging trends in AI ML LLMs and MLOps to continuously enhance solution quality and team capabilityCustomer Partner EngagementCollaborate directly with customer technical experts and partners eg Google to design solutions that meet business goals and compliance demonstrating technical and business maturityQualificationsAdvanced hands on experience in Machine Learning LLMs and MLOpsProven expertise in Google Vertex AI Python and Google CCAI CES platformsDeep understanding of AI governance AIRB security and responsible AI principlesExperience in model evaluation frameworks agentic architectures and RAG pipelinesStrong communication and collaboration skills for engaging with cross functional teams vendors and stakeholdersDemonstrated ability to balance innovation with operational rigor in production grade AI environments '841200

Requirements

Advanced hands on experience in Machine Learning LLMs and MLOps
Proven expertise in Google Vertex AI Python and Google CCAI CES platforms
Deep understanding of AI governance AIRB security and responsible AI principles
Experience in model evaluation frameworks agentic architectures and RAG pipelines
Strong communication and collaboration skills for engaging with cross functional teams vendors and stakeholders
Demonstrated ability to balance innovation with operational rigor in production grade AI environments

Nice To Haves

Experience with Parameter Efficient Fine Tuning PEFT is a plus

Responsibilities

Lead end to end AI development, balancing model design and implementation with guiding AI teams.
Design, build, and deploy production grade machine learning models, including LLMs on Google Cloud Platform Vertex AI using Python.
Partner with internal AI teams, Google, and other vendors to drive innovation and align with best practices.
Own and execute AI governance processes, including AI Risk Board (AIRB) reviews, data security compliance, and ethical AI considerations.
Design and implement models for Conversational AI using Google CCAI/CES components and lead model evaluation.
Apply agentic architectures and Retrieval Augmented Generation (RAG) to build intelligent AI systems.
Optimize and fine tune models for performance, efficiency, scalability, and accuracy.
Work with stakeholders and engineering teams to translate business problems into AI solutions.
Stay ahead of emerging trends in AI/ML/LLMs and MLOps to enhance solution quality and team capability.
Collaborate with customer technical experts and partners (e.g., Google) to design solutions meeting business goals and compliance.