Quantiphi

AI Research Scientist

• Deliver multi-geography projects for healthcare organizations
• Collaborate with Cloud, Software, and Data Engineering teams
• Design, build, and evaluate healthcare solutions
• Develop high-level solution architectures
• Ensure responsible AI practices
• Lead technical discussions and mentor senior resources
• Contribute to a collaborative team culture

Architect ML – AI Researcher

Backend Engineer

• Lead the design, development, and maintenance of scalable Ruby on Rails applications
• Configure, optimize, and scale application servers such as Puma, Unicorn, Falcon, or similar for high-performance systems
• Architect and deploy Rails applications using AWS services (ECS, EKS, EC2, RDS, S3, CloudFront, IAM)
• Build, maintain, and scale Docker-based microservices using Kubernetes orchestration
• Drive migration projects from Blackboard to Canvas LMS
• Collaborate with DevOps and product teams on CI/CD, infrastructure as code, and monitoring
• Optimize application performance using caching and background processing systems (Redis, Sidekiq, SQS, Kafka)
• Implement monitoring and alerting systems (CloudWatch, New Relic, Prometheus, Grafana)
• Participate in code reviews, provide technical leadership, and mentor junior engineers

Associate Architect – Software Development, Ruby on Rails

Machine Learning Engineer

• Work as an Associate Architect - Machine Learning (AWS)
• Collaborate with cross-functional teams to deliver cloud ML solutions
• Implement and develop machine learning models on AWS
• Optimize and evaluate machine learning workflows and models
• Design and maintain software architecture for cloud-based applications

Associate Architect – Machine Learning

Sales

• Formulate a sales strategy for each AWS Public Sector target segment including Higher Education, State & Local Government, Edtech/GovTech, and NPO
• Drive the sales of professional services, accelerators and solutions, forecast accurately and achieve strategic goals by leading customers through the entire business cycle.
• Build and deepen executive relationships within the AWS regional and practice ecosystem to drive new business opportunities.
• Influence long-term strategic direction with customers and serve as a business partner to them
• Manage entire complex sales-cycles, often presenting to C-level executives.
• Lead account strategy in generating and developing business growth opportunities, working collaboratively with Internal GTM and AWS alliance teams - all to maximize business results in territory and open up opportunities with customers.
• Actively understand each customer’s technology footprint, strategic growth plans and business drivers, technology strategy and landscape.
• Create proposals and Statements of Work in collaboration with the Delivery team
• Drive deal profitability
• Measure customer satisfaction and be the ‘voice of the customer’

National Public Sector Sales Lead – AWS Practice

Platform Engineer

• Architect and implement the MLOps strategy for the program
• Design and own enterprise-grade ML/LLM pipelines
• Build container-oriented ML platforms (GKE-first)
• Implement hybrid MLOps + LLMOps workflows
• Serve as a technical authority across multiple internal and customer projects
• Enable observability, monitoring, drift detection, lineage tracking, and auditability across ML/LLM systems
• Collaborate with cross-functional teams to deliver production-ready ML solutions
• Ensure all solutions adhere to security, governance, and compliance expectations
• Conduct architecture reviews, troubleshoot complex ML system issues, and guide teams through implementation
• Mentor engineers and provide guidance on modern MLOps tools

Architect – Platform Engineering

DevOps Engineer

• Lead design and implementation of a unified observability platform
• Architect observability pipeline leveraging AWS and open-source tools
• Deploy and configure tools like Prometheus, Grafana, and Splunk
• Build scalable monitoring and logging systems

DevOps Engineer, Observability

• We are seeking a highly skilled and innovative Machine Learning Engineer with 4-6 years of experience, specializing in Generative AI and Agentic AI, to join our dynamic team.
• This role is critical for designing, developing, deploying, and maintaining advanced AI solutions that leverage large language models (LLMs) to create intelligent, autonomous agents capable of performing complex tasks and driving significant business value within the telecom sector.
• The ideal candidate will possess extensive hands-on experience across the entire Generative AI lifecycle, with a strong focus on MLOps, performance monitoring, and ensuring the robustness and scalability of AI systems in production.
• Collaborate closely with data scientists, product managers, and other engineering teams to define requirements, design solutions, and deliver high-impact AI products.
• Develop strategies for continuous improvement, model retraining, and A/B testing in production environments.

Senior Machine Learning Engineer

Product Manager

• Lead the design and definition of end-to-end user journeys
• Own the product roadmap from conception to delivery
• Serve as the primary point of contact for Business Units
• Strategize and execute the rollout of new features
• Provide high-level strategic oversight for the platform
• Work closely with technical teams to understand architectural constraints
• Proactively identify potential bottlenecks in the product lifecycle

• Architect, build, and deploy production-grade agentic AI systems
• Design and develop end-to-end multi-agent systems
• Own the deployment, scaling, and maintenance of agentic systems on major cloud platforms
• Integrate LLMs to serve as the core reasoning engines

Technical Architect – Machine Learning

Cloud Engineer

• Lead application migration and modernization initiatives for enterprise workloads transitioning to AWS.
• Define and execute strategies across rehost, replatform, and refactor approaches based on application architecture and business needs.
• Conduct application discovery, codebase assessment, and dependency mapping to inform migration and modernization decisions.
• Design and implement cloud-native application architectures using microservices and serverless patterns.
• Refactor monolithic applications into modular, scalable components where required.
• Drive modernization of application code, frameworks, and runtime environments (.NET/Java/Python).
• Integrate and enhance CI/CD pipelines to support modern application development and deployment.
• Collaborate with cross-functional teams to ensure seamless application transformation and delivery.
• Provide technical leadership and mentorship while remaining hands-on with design and development.

Cloud Application Architect

AI Engineer

• Architect and build full-stack, enterprise-grade platforms using reusable and modular agentic workflow patterns
• Translate inputs from internal product teams into scalable, AI-powered workflows and services
• Design and implement agentic workflows, including task decomposition and agent orchestration
• Apply enterprise integration patterns to connect AI workflows with internal platforms and external systems
• Guide and supervise coding assistants and AI agents to accelerate delivery
• Design and operate event-driven architectures using AWS native services
• Build secure Python back-end services for AI agents and enterprise integrations
• Develop React-based front-end applications supporting complex user workflows
• Build and operate multi-tenant architectures with security and compliance

Network Engineer

• Join a trailblazing team that’s shaping the future of AI, ML, and cloud innovation.
• Collaborate with teams and deliver enterprise-grade AI services and solutions that create real-world impact.
• Work in a diverse culture built on transparency, diversity, integrity, learning, and growth.
• Solve what truly matters for organizations by reimagining their businesses through intelligent, scalable, and transformative AI.

Marketing

• Own the end-to-end marketing roadmap for your Business Unit
• Build compelling value propositions for our industry offerings
• Monitor trends and competitor solution positioning
• Support the Account-Based Marketing (ABM) and field marketing engine
• Own the 'shop window' of the BU
• Manage end-to-end communications for our global partners
• Participate in the scoping and implementation of AI-Native Marketing workflows

Senior Specialist – Solution Marketing, AWS

• Design and implement GenAI solutions using AWS Bedrock and Agentcore.
• Define architecture for LLM-based applications, including RAG pipelines and agentic workflows.
• Develop and orchestrate agentic AI workflows, enabling multi-step reasoning, tool usage, and task automation.
• Build and manage RAG pipelines, including embeddings, retrieval mechanisms, and vector databases.
• Integrate LLM capabilities into enterprise applications via APIs and backend services.
• Design and optimize prompt engineering strategies for accuracy, relevance, and performance.
• Work with structured and unstructured data sources to enable knowledge-driven AI applications.
• Ensure model evaluation, monitoring, and optimization for latency, cost, and response quality.
• Collaborate with application, data, and platform teams for end-to-end solution delivery.
• Define best practices for security, governance, and responsible AI usage.
• Troubleshoot and resolve issues in production GenAI systems.
• Provide technical leadership and mentor team members while remaining hands-on.

GenAI Architect

Data Scientist

• Lead end-to-end data science initiatives for predictive analytics use cases such as demand forecasting, churn prediction, and risk modeling
• Translate business requirements into ML problem statements and define appropriate modeling approaches
• Design, build, and deploy machine learning models using traditional ML techniques (regression, classification, clustering, time series)
• Drive feature engineering, data preparation, and exploratory data analysis to improve model performance
• Develop and manage scalable ML pipelines from data ingestion to model deployment
• Deploy and manage models on AWS using services such as SageMaker
• Ensure model performance through validation, monitoring, and periodic retraining
• Collaborate with data engineering and MLOps teams to productionize ML solutions
• Apply best practices for model governance, explainability, and responsible AI
• Mentor junior data scientists and provide technical leadership while remaining hands-on
• Communicate insights, model outputs, and recommendations effectively to business stakeholders