Jobs at Quantiphi

Lead platform engineering and cloud migration solutions, primarily moving VMware workloads from on-premises data centers to AWS. Architect and manage enterprise AWS infrastructure, hybrid networking, infrastructure-as-code, automation, CI/CD, security, monitoring, and container platforms. Use migration assessment and execution tools, troubleshoot Windows and Linux systems, and support database and application migrations. Collaborate on scalable, secure cloud architectures across application, data, analytics, and machine learning environments.

Supports PMO resource allocation, scheduling, project billing, invoicing, revenue tracking, and financial reconciliation. Maintains resource and financial data in Workday, generates utilization and revenue reports, coordinates with project managers and Finance, resolves discrepancies, and improves PMO processes. The role requires strong spreadsheet, reporting, communication, organizational, and problem-solving skills, with experience in professional services project administration and Workday Financials or Projects.

Administers and optimizes Workday by configuring business processes, workflows, calculated fields, security, reports, and dashboards. Evaluates enhancements, supports releases, troubleshoots user issues, performs functional testing, maintains system integrity, and collaborates with stakeholders to implement business requirements. Provides end-user support and contributes to Workday community activities.

Provides 24/7 production support for AWS-based web applications and microservices. Responsibilities include debugging and resolving application and infrastructure issues, analyzing logs and metrics, optimizing performance, monitoring systems, addressing vulnerabilities, enhancing functionality, documenting procedures, and collaborating with developers, DevOps, QA, data engineers, project managers, clients, and other stakeholders.

Lead business discovery and requirements gathering for an insurance transformation program spanning Claims, Underwriting, and Finance. Create functional specifications, user stories, source-to-target mappings, data transformation rules, business validations, data lineage, and quality controls. Partner with stakeholders, product owners, architects, engineers, and testing teams throughout Agile delivery, facilitating Scrum ceremonies, UAT, backlog refinement, dependency management, and solution reviews.

Lead the vision and roadmap for AI/ML and agentic systems, managing research backlogs and translating advanced concepts into product requirements. Partner with researchers, engineers, architects, and executives to move initiatives from proof of concept through production on GCP and Kubernetes. Define evaluation frameworks, KPIs, and benchmarks while coordinating SAFe Agile planning, dependencies, scalability, security, CI/CD, and product adoption.

Build and deploy end-to-end machine learning solutions, with a strong focus on computer vision, image processing, model development, feature engineering, cloud platforms, MLOps, monitoring, and CI/CD. Collaborate with stakeholders and IT teams across multiple engagements, follow Agile methodologies, ensure project delivery quality, and contribute to research and development.

Design, architect, and implement highly available video streaming and inference platforms. Build and maintain Kubernetes and Docker infrastructure, CI/CD pipelines, monitoring, alerting, deployment strategies, and distributed systems. Develop production-grade Python backend services using Flask and Gunicorn, integrate databases and streaming pipelines such as Redis and Kafka, and troubleshoot Linux-based container environments. Collaborate with machine learning research and production DevOps/MLOps teams.

Leads delivery of large-scale AWS and digital transformation programs, overseeing planning, execution, reporting, risk mitigation, quality, customer outcomes, and continuous improvement. Manages multi-functional teams, develops product and service roadmaps, identifies business opportunities, resolves customer issues, and coaches or recruits team members. Collaborates with engineering and enterprise stakeholders while applying agile methodologies, business analysis, and emerging AI, machine learning, data, and cloud technologies.

Lead Quantiphi’s co-sell motion with frontier AI partners including OpenAI, Anthropic, and LangChain. Build partner-seller relationships, originate and qualify enterprise AI opportunities, coordinate multidisciplinary pursuit teams, manage account and pipeline reviews, and drive proposals and commercial closures. Translate customer AI adoption needs into implementation services engagements, support production deployments, and develop repeatable sales plays and expansion opportunities.

Architect and build cloud-native data platforms, Snowflake pipelines, and datalakes for complex financial datasets. Lead legacy modernization, ETL/ELT, reconciliation, data quality, governance, and regulatory reporting initiatives. Drive technical product roadmaps, backlog prioritization, requirements, testing, cutovers, and releases. Coordinate Agile delivery through Jira and Confluence while aligning business, analytics, and engineering stakeholders across the full data and software lifecycle.

Lead DevOps Engineer responsible for designing and managing GitOps and CI/CD pipelines, release workflows, Terraform infrastructure, Kubernetes platforms, observability, cloud networking, security, and cost optimization. The role leads Azure-to-GCP migrations, administers cloud policies and access controls, supports production systems, mentors engineers, and drives Agile release and continuous-improvement practices.

Own backend and infrastructure architecture while developing Python and PHP services, APIs, authentication integrations, and database solutions. Design and maintain GCP infrastructure, Docker environments, CI/CD pipelines, monitoring, security, backups, and disaster recovery. Lead technical decisions, cloud optimization, incident response, documentation, and collaboration with frontend teams on headless CMS and SaaS deployments. Advise stakeholders on technical approaches, costs, risks, and estimates while mentoring engineers and supporting client-facing initiatives.

Lead cloud migration and platform engineering efforts, designing AWS architectures and migration plans from on-prem VMware to AWS. Perform migration assessments, tooling selection, wave planning, dependency analysis, security/compliance alignment, OS and database troubleshooting, and optimize post-migration performance while collaborating with stakeholders and platform teams.

Designs, develops, optimizes, and deploys advanced machine learning and generative AI solutions on AWS. Builds RAG pipelines, AI agents, knowledge bases, and workflow orchestration using Bedrock and agentic AI frameworks. Responsibilities include document processing, vector search, prompt engineering, model integration, guardrails, authentication, scalable architecture, monitoring, and secure production deployment.

Design and implement resilient, secure services, adapters, and agents using C# and .NET. Modernize legacy .NET Framework applications, develop REST APIs, integrate databases and messaging, and ensure strong observability, reliability, and test coverage. Support Windows, Linux, and containerized deployments, participate in design reviews and releases, and mentor peers. The role also involves preserving legacy behavior during migrations and building unattended components for customer environments.

Drive end-to-end B2B sales and go-to-market activities for data science and applied AI solutions. Responsibilities include prospecting, customer relationship management, technical presentations, solution scoping, proposal development, business analysis, and resolving technical objections. The role partners with solution architects and data science teams to develop AI and cloud solutions, demonstrate Google Cloud integrations, manage client expectations, and identify new business opportunities across multiple industries. Travel to customer sites and events may be required.

Analyzes complex insurance pricing, delegated authority, and exposure datasets; profiles data, evaluates models, creates source-to-target mappings, documents lineage and definitions, performs quality checks and reconciliations, identifies gaps, and translates findings into requirements and recommendations for business, engineering, and technology teams.

Architect and implement enterprise MLOps and LLMOps platforms on Google Cloud. Own model lifecycle pipelines, CI/CD automation, deployment, monitoring, drift detection, lineage, governance, and observability. Build GKE-based ML platforms, conduct architecture reviews, troubleshoot complex systems, collaborate with engineering and client teams, and mentor technical staff. Travel up to 30% is required.

Architect and build production-grade multi-agent AI systems, agentic workflows, RAG solutions, tool libraries, memory systems, and evaluation frameworks. Deploy scalable, low-latency solutions across AWS, GCP, or Azure using MLOps, CI/CD, observability, and infrastructure automation. Integrate and optimize LLMs, implement monitoring and safety controls, establish performance metrics, and lead technical delivery while mentoring team members and communicating complex AI concepts to stakeholders.