Quantiphi

• 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’

• 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

• 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

• Building cloud-native, hybrid, or multi-cloud solutions • Developing data pipelines using PySpark with Databricks • Collaborating with teams to deliver technical workloads • Creating roadmaps and architecture for migration workloads on AWS • Mentoring and managing data engineering teams

Role Description As Senior Technical Architect - Databricks at Quantiphi, you will solve enterprise data problems and will develop solutions for migration, storage, and processing. Qualifications - 12+ years of relevant experience in building cloud-native, hybrid, or multi-cloud solutions including Databricks and AWS. - Experience with Databricks implementations - developing data pipelines using PySpark. - Experience in Databricks Workspaces, Notebooks, Delta Lake, APIs. - Expertise in production grade solutions using: - AWS (Redshift, S3, Glue, Lambda, Airflow) - PySpark - Python - Data pipelines using Glue/Airflow/Sagemaker. - Hands-on experience in working on large cloud-based migration workloads involving SQL and NoSQL Databases. - Experience with migration of databases to AWS. - Strong ability in creating roadmaps and architecture to execute migration workloads on AWS. - Exposure to ETL tools and Data warehouse. - Experience in SQL and Query optimisation. - Proficient in SQL-based technologies (MySQL, Oracle DB, SQL Server etc.). - Experience in creation and maintenance of data dictionaries, metadata repositories, and data lineage documentation. - Experience building and supporting large-scale systems in a production environment. - Strong skills to mentor and manage teams of junior and senior data engineers and leading end to end delivery of technical workloads. - Experience using Github/codecommit for developmental activities. - Experience designing and optimizing orchestration frameworks using Apache Airflow / Amazon MWAA including DAG optimization, dependency management, retries, monitoring, and operationalization. Requirements - Experience with AWS services - S3, Redshift, Secrets Manager. - Exposure to SageMaker Unified Studio (SMUS) and modern business catalog/governance implementations for metadata management, data discovery, and governed analytics access. - Experience in implementing data integration projects using ETL. - Experience in using Airflow or Step Functions for orchestration. - Exposure to IaC tools like Terraform and to CI/CD tools. - Prior experience in migrating (on-prem to cloud) and processing large amounts of data. - Experience in implementing data lake and data warehouse on cloud. - Experience in implementing the industry's best practices. Benefits - Join one of the world’s fastest-growing AI-first digital engineering companies and make a real impact at scale. - Lead and collaborate with a high-energy team of talented, driven individuals solving complex, meaningful challenges. - Work with Fortune 500 companies and disruptive innovators in a research-driven environment with 60+ patents. - Stay ahead of the curve by gaining hands-on experience with cutting-edge AI, ML, data, and cloud technologies while continuously upskilling. - If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

• 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.

• 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

Role Description Elastic Kubernetes Service (EKS) Specialist Experience Level: 8+ years Employment type: Full Time Location: Remote (USA) Key Responsibilities - Strategic Landscape Assessment & Consolidation: - Lead Enterprise EKS Audit: Conduct a comprehensive architectural assessment of the current environment comprising 20+ disparate EKS clusters, analyzing workload distribution, compute utilization, and AWS security postures. - Formulate Consolidation Roadmap: Architect a strategic consolidation plan to migrate workloads from legacy clusters into a streamlined, highly available, and cost-optimized multi-tenant EKS platform. - Enterprise Platform Architecture: - Define CNI & Service Mesh Strategy: Serve as the technical authority to evaluate and resolve complex architectural decisions regarding Container Network Interfaces (e.g., AWS VPC CNI vs. Cilium) and Service Mesh implementations, optimizing for network performance, observability, and IPAM constraints. - Architect the Golden Cluster Standard: Design and establish the standardized, production-ready "Golden Cluster" architecture. Develop the foundational Terraform frameworks to automate the deployment of the EKS control plane, VPC infrastructure, Karpenter/node groups, and core add-ons. - Design Advanced Traffic & Storage Patterns: Architect a highly resilient ingress strategy utilizing the AWS Load Balancer Controller for secure external traffic routing, and establish enterprise patterns for persistent storage using EFS and EBS CSI drivers. - Security, Governance & Pilot Execution: - Establish Zero-Trust Security Postures: Enforce strict least-privilege access by mandating EKS Pod Identity or IRSA across the platform. Architect secure cluster communications using Security Groups for Pods and strict Kubernetes Network Policies. - Lead Architectural Validation: Define the scope and success criteria for pilot migrations. Oversee the end-to-end deployment and validation of representative Python microservice workloads to prove out the AWS architecture, ingress, and mesh capabilities. - Lifecycle Governance & Platform Scalability: - Define Cluster Lifecycle Frameworks: Establish enterprise governance for zero-downtime EKS version upgrades, AMI lifecycles, and create comprehensive architectural runbooks for platform operations teams. - Architect Disaster Recovery: Design and integrate enterprise-grade backup, restore, and disaster recovery capabilities for cluster state and persistent volumes using Velero and Amazon S3. - Design CI/CD Infrastructure: Partner with engineering leadership to architect and deploy dynamic, scalable CI/CD execution environments (e.g., GitHub Actions Runner Controller) directly on the EKS platform. Qualifications - Deep expertise in designing multi-tenant, highly available EKS environments at enterprise scale, including control plane architecture, compute strategies, and Kubernetes core governance (RBAC, Pod Security Standards). - Expert-level understanding of AWS VPC topologies, Transit Gateway, and the strategic selection/implementation of CNIs (AWS VPC CNI, Cilium, Calico) to solve complex IPAM, scaling, and performance challenges. - Authoritative knowledge of AWS IAM integration with Kubernetes, specifically architecting IAM Roles for Service Accounts (IRSA), EKS Pod Identity, and AWS KMS encryption strategies. - Proven experience architecting enterprise ingress patterns (AWS Load Balancer Controller, API Gateways) and designing Service Mesh architectures (Istio Ambient/Sidecar, Cilium Mesh) for zero-trust networking. - Advanced Terraform expertise, focusing on designing modular, scalable, and compliant IaC frameworks for EKS fleet management and foundational add-ons. Good-to-Have - Proven track record of leading massive cluster consolidation programs, migrating sprawling multi-cluster environments into streamlined, cost-optimized architectures. - Deep experience with advanced AWS compute scaling and cost-optimization strategies, including Karpenter, Spot Instances, and Graviton (ARM) processors. - Experience architecting AWS persistent storage (EBS/EFS CSI drivers) and enterprise disaster recovery frameworks (Velero backing up to Amazon S3). - Experience designing scalable, containerized CI/CD runner infrastructure (e.g., GitHub Actions Runner Controller) directly on EKS to support developer platforms. Benefits - Make an impact at one of the world’s fastest-growing AI-first digital engineering companies. - Upskill and discover your potential as you solve complex challenges in cutting-edge areas of technology alongside passionate, talented colleagues. - Work where innovation happens - work with disruptive innovators in a research-focused organization with 60+ patents filed across various disciplines. - Stay ahead of the curve—immerse yourself in breakthrough AI, ML, data, and cloud technologies and gain exposure working with Fortune 500 companies. - If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

• 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