Quantiphi

We are seeking a skilled Data Engineer to join the AI Platform Capabilities team supporting the UDP Uplift program.

In this role, you will design, build, and test standardized data and AI platform capabilities across a multi-cloud environment (Azure & GCP).

You will collaborate closely with AI use case teams to develop:

• Scalable data pipelines
• Reusable data products
• Foundational data infrastructure

Your work will support advanced AI solutions such as:

• GenAI
• RAG (Retrieval-Augmented Generation)
• Document Intelligence

Key Responsibilities

• Design and develop scalable ETL/ELT pipelines for AI workloads
• Build and optimize data pipelines for structured & unstructured data
• Enable context processing & vector store integrations
• Support streaming data workflows and batch processing
• Ensure adherence to enterprise data models, governance, and security standards
• Collaborate with DataOps, MLOps, Security, and business teams (LBUs)
• Contribute to data lifecycle management for AI platforms

Required Skills

• 5–7 years of hands-on experience in Data Engineering
• Strong expertise in Python and advanced SQL
• Experience with GCP and/or Azure cloud-native data services
• Hands-on experience with PySpark / Spark SQL
• Experience building data pipelines for ML/AI workloads
• Understanding of CI/CD, Git, and Agile methodologies
• Knowledge of data quality, governance, and security practices
• Strong collaboration and stakeholder management skills

Nice-to-Have Skills

• Experience with Vector Databases / Vector Stores (for RAG pipelines)
• Familiarity with MLOps / GenAIOps concepts (feature stores, model registries, prompt management)
• Exposure to Knowledge Graphs / Context Stores / Document Intelligence workflows
• Experience with DBT (Data Build Tool)
• Knowledge of Infrastructure-as-Code (Terraform)
• Experience in multi-cloud deployments (Azure + GCP)
• Familiarity with event-driven systems (Kafka, Pub/Sub) & API integrations

Ideal Candidate Profile

• Strong data engineering foundation with AI/ML exposure
• Experience working in multi-cloud environments
• Ability to build production-grade, scalable data systems
• Comfortable working in cross-functional, fast-paced environments

We are looking for a QA Engineer with hands-on experience in testing Generative AI systems, LLMs, and RAG pipelines. This role goes beyond traditional QA and focuses on evaluating non-deterministic AI outputs, testing agentic workflows, and ensuring AI safety, accuracy, and reliability in enterprise-grade AI services.

You will work on API-driven AI services such as Intelligent Document Processing and AI Gateways, ensuring they meet enterprise standards before deployment.

Key Responsibilities

• Test and validate Generative AI applications, LLMs, and RAG-based systems
• Evaluate AI outputs for accuracy, groundedness, coherence, and hallucination
• Design and execute test strategies for multi-step agentic workflows
• Perform API and integration testing for AI services
• Build automated test pipelines using Python
• Create synthetic datasets for testing AI systems
• Conduct adversarial testing (prompt injection, safety, guardrails)
• Integrate AI testing into CI/CD pipelines

Must-Have Skills

• 5–7 years of experience in QA / Test Automation
• Hands-on experience testing Generative AI / LLM-based applications
• Strong programming skills in Python
• Experience with RAG pipelines
• Knowledge of LLM evaluation frameworks (RAGAS, TruLens, LangSmith or similar)
• Strong experience in API testing (Postman, REST Assured, etc.)
• Experience testing multi-agent workflows / agentic systems
• Understanding of hallucination, prompt injection, and AI safety concepts

Good-to-Have Skills

• Experience with GCP (Vertex AI) or Azure OpenAI
• SQL / NoSQL knowledge for data validation
• Experience in BFSI / Insurance domain
• Performance testing of APIs and AI systems

Additional Information

• Candidates without hands-on experience in testing Generative AI / LLM systems will not be considered
• Immediate to 45 days notice period preferred 

We are looking for a hands-on Generative AI Engineer to design, build, and deploy enterprise-grade GenAI platform capabilities across multiple business units.

This role focuses on developing scalable, reusable AI components across the full stack—covering RAG systems, agent orchestration, LLM infrastructure, and GenAIOps—on GCP (primary) and Azure.

This is a core engineering role, not a research or client-facing position.

Key Responsibilities

• Design and build production-ready GenAI systems and platform components
• Develop and deploy RAG pipelines (data ingestion, embeddings, retrieval, APIs)
• Implement agent-based architectures (orchestration, routing, memory, workflows)
• Build and manage LLM infrastructure (model routing, caching, rate limiting, observability)
• Develop scalable APIs and services for AI capabilities
• Implement GenAIOps/MLOps practices (prompt management, evaluation, monitoring, deployment)
• Work with GCP services (Vertex AI, BigQuery, Cloud Run, GKE, Pub/Sub) to deploy solutions
• Ensure AI safety, governance, and compliance (PII protection, guardrails, auditability)
• Collaborate with cross-functional teams to deliver reusable, enterprise-grade solutions

Required Skills & Experience

• Strong hands-on experience in Generative AI and RAG systems (production level)
• Experience building multi-agent or agentic AI systems
• Proficiency in Python and backend/API development
• Hands-on experience with GCP AI/ML ecosystem (Vertex AI, BigQuery, etc.)
• Solid understanding of LLM infrastructure and orchestration layers
• Experience with CI/CD pipelines and Infrastructure as Code (Terraform)
• Knowledge of GenAIOps/MLOps practices and model lifecycle management
• Understanding of AI safety, governance, and compliance

Nice to Have

• Experience with LangChain, LlamaIndex, or similar frameworks
• Familiarity with RAG evaluation tools (RAGAS, DeepEval)
• Knowledge of Knowledge Graphs with RAG
• Experience in multi-cloud environments (GCP + Azure)
• Exposure to BFSI/regulated domains

What We’re Looking For

• Engineers who have built and deployed real-world GenAI systems at scale
• Strong backend and systems-thinking mindset
• Ability to work in fast-paced, enterprise environments

Role Type

• Individual Contributor (IC)
• Platform Engineering / Backend-heavy GenAI role

We are looking for a Mid-Level .NET Developer to design, develop, and maintain scalable microservices for enterprise applications. The role involves working on high-performance, reliable systems deployed in containerized environments.

Key Responsibilities:

• Develop and maintain scalable .NET microservices
• Build robust Web APIs with proper validation, error handling, and security
• Write unit and integration tests to ensure code quality
• Design portable and environment-agnostic solutions
• Collaborate with cross-functional teams and client stakeholders
• Optimize performance and implement caching strategies
• Follow security best practices for enterprise applications
• Participate in code reviews and maintain coding standards
• Support deployment and troubleshoot issues in client environments

Must-Have Skills:

Core Technical Expertise:

• 4+ years of experience with .NET Core (3.1+) / .NET 5+ and C# (8+)
• Strong hands-on experience with ASP.NET Core Web API & Entity Framework Core
• Experience building REST APIs and middleware
• Strong understanding of SOLID principles, Dependency Injection, Repository pattern
• Experience with unit testing (xUnit / NUnit / MSTest), Moq, integration testing

Microservices & Deployment:

• Hands-on experience with Docker
• Understanding of microservices architecture & distributed systems
• Experience with configuration management (appsettings.json, IConfiguration)
• Knowledge of NuGet and dependency management

Good-to-Have Skills:

Advanced Technical:

• Experience with .NET 6/7/8, Minimal APIs, gRPC, SignalR
• Advanced EF Core, Dapper, database migrations
• Kubernetes and container orchestration
• Cloud platforms: Azure / GCP / Alibaba Cloud
• Message brokers: Azure Service Bus, RabbitMQ, Kafka
• Databases: PostgreSQL, MySQL, MongoDB, Cassandra
• API Gateways: Azure API Management, Kong

Development & Operations:

• CI/CD tools: Azure DevOps, Jenkins, GitHub Actions
• Monitoring: Application Insights, Serilog, Prometheus
• Security: HTTPS, CORS, input validation, secure coding
• Background services: Hangfire, Quartz.NET

Client-Facing Experience:

• Experience in service-based organizations
• Ability to adapt to multiple domains
• Understanding of industry standards and compliance 

We are looking for a Mid-Level .NET Developer to design, develop, and maintain scalable microservices for enterprise applications. The role involves working on high-performance, reliable systems deployed in containerized environments.

Key Responsibilities:

• Develop and maintain scalable .NET microservices
• Build robust Web APIs with proper validation, error handling, and security
• Write unit and integration tests to ensure code quality
• Design portable and environment-agnostic solutions
• Collaborate with cross-functional teams and client stakeholders
• Optimize performance and implement caching strategies
• Follow security best practices for enterprise applications
• Participate in code reviews and maintain coding standards
• Support deployment and troubleshoot issues in client environments

Must-Have Skills:

Core Technical Expertise:

• 4+ years of experience with .NET Core (3.1+) / .NET 5+ and C# (8+)
• Strong hands-on experience with ASP.NET Core Web API & Entity Framework Core
• Experience building REST APIs and middleware
• Strong understanding of SOLID principles, Dependency Injection, Repository pattern
• Experience with unit testing (xUnit / NUnit / MSTest), Moq, integration testing

Microservices & Deployment:

• Hands-on experience with Docker
• Understanding of microservices architecture & distributed systems
• Experience with configuration management (appsettings.json, IConfiguration)
• Knowledge of NuGet and dependency management

Good-to-Have Skills:

Advanced Technical:

• Experience with .NET 6/7/8, Minimal APIs, gRPC, SignalR
• Advanced EF Core, Dapper, database migrations
• Kubernetes and container orchestration
• Cloud platforms: Azure / GCP / Alibaba Cloud
• Message brokers: Azure Service Bus, RabbitMQ, Kafka
• Databases: PostgreSQL, MySQL, MongoDB, Cassandra
• API Gateways: Azure API Management, Kong

Development & Operations:

• CI/CD tools: Azure DevOps, Jenkins, GitHub Actions
• Monitoring: Application Insights, Serilog, Prometheus
• Security: HTTPS, CORS, input validation, secure coding
• Background services: Hangfire, Quartz.NET

Client-Facing Experience:

• Experience in service-based organizations
• Ability to adapt to multiple domains
• Understanding of industry standards and compliance 

Responsible for developing, enhancing, modifying, and maintaining chatbot applications in the Global Markets environment. The role involves designing, coding, testing, debugging, and documenting conversational AI solutions, along with supporting activities aligned to the corporate systems architecture.

You will work closely with business partners to understand requirements, analyze data, and deliver optimal, market-ready conversational AI and automation solutions.

Key Responsibilities

• Design, develop, test, debug, and maintain chatbot and virtual agent applications
• Collaborate with business stakeholders to define and translate requirements into technical solutions
• Analyze large volumes of conversational data to improve chatbot accuracy and performance
• Develop automation workflows for data handling and refinement
• Train and optimize chatbots using historical chat logs and user-generated content
• Ensure solutions align with enterprise architecture and best practices
• Document solutions, workflows, and technical designs clearly

Required Skills

• Hands-on experience in developing virtual agents (chatbots/voicebots) and Natural Language Processing (NLP)
• Experience with one or more AI/NLP platforms such as:
• Dialogflow, Amazon Lex, Alexa, Rasa, LUIS, Kore.AI
• Microsoft Bot Framework, IBM Watson, Wit.ai, Salesforce Einstein, Converse.ai
• Strong programming knowledge in Python, JavaScript, or Node.js
• Experience training chatbots using historical conversations or large-scale text datasets
• Practical knowledge of:
• Formal syntax and semantics
• Corpus analysis
• Dialogue management
• Strong written communication skills
• Strong problem-solving ability and willingness to learn emerging technologies

Nice-to-Have Skills

• Understanding of conversational UI and voice-based processing (Text-to-Speech, Speech-to-Text)
• Experience building voice apps for Amazon Alexa or Google Home
• Experience with Test-Driven Development (TDD) and Agile methodologies
• Ability to design and implement end-to-end pipelines for AI-based conversational applications
• Experience in text mining, hypothesis generation, and historical data analysis
• Strong knowledge of regular expressions for data cleaning and preprocessing
• Understanding of API integrations, SSO, and token-based authentication
• Experience writing unit test cases as per project standards
• Knowledge of HTTP, REST APIs, sockets, and web services
• Ability to perform keyword and topic extraction from chat logs
• Experience training and tuning topic modeling algorithms such as LDA and NMF
• Understanding of classical Machine Learning algorithms and appropriate evaluation metrics
• Experience with NLP frameworks such as NLTK and spaCy

We are hiring an Associate Technical Architect with strong expertise in Azure-based data platforms to design scalable data lakes, data warehouses, and enterprise data pipelines, while working with global teams.

Key Responsibilities

• Design and implement scalable data lake, data warehouse, and lakehouse architectures on Azure
• Build resilient data pipelines using Azure services
• Architect and optimize cloud-based data platforms
• Improve large-scale data processing and query performance
• Collaborate with engineering teams, QA, product managers, and stakeholders
• Communicate technical roadmap, risks, and mitigation strategies

Must-Have Skills:

• 6+ years of experience in Azure Data Engineering / Data Architecture

Azure Data Platform

• Experience with Azure Data Factory
• Hands-on with Azure Databricks and PySpark
• Experience with Azure Data Lake Storage
• Knowledge of Azure Synapse or Azure SQL for data warehousing

Programming & Data Skills

• Strong programming skills in Python and PySpark
• Advanced SQL with query optimization and performance tuning
• Experience building ETL / ELT data pipelines

Data Architecture Knowledge

• Understanding of MPP databases
• Knowledge of partitioning, indexing, and performance optimization
• Experience with data modeling (dimensional, normalized, lakehouse)

Cloud Fundamentals

• Azure security, networking, scalability, and disaster recovery
• Experience with on-premise to Azure migrations

Certification (Preferred)

• Azure Data Engineer or Azure Solutions Architect certification

Good-to-Have Skills

• Domain experience in FSI, Retail, or CPG
• Exposure to data governance tools
• Experience with BI tools such as Power BI or Tableau
• Familiarity with Terraform, CI/CD pipelines, or Azure DevOps
• Experience with NoSQL databases such as Cosmos DB or MongoDB

Soft Skills

• Strong problem-solving and analytical thinking
• Good communication and stakeholder management
• Ability to translate technical concepts into business outcomes
• Experience working with global or distributed teams

Must have skills:

● Experience: 6+ years of hands-on experience in Cloud Platform Engineering, DevOps, or Site Reliability Engineering (SRE).

● Multi-Cloud Infrastructure: Proficiency in architecting, deploying, and maintaining cloud infrastructure across GCP and Azure (VPC, IAM, Cloud Storage/Blob, Cloud Run/Functions, Pub/Sub, GKE/AKS, Cloud SQL).

● Container Orchestration: Extensive experience with Kubernetes (GKE or AKS) and Docker for managing and scaling containerized applications.

● Infrastructure as Code (IaC) & Automation: Strong proficiency using Terraform along with Python and Bash/Shell scripting for infrastructure automation.

● CI/CD Automation: Experience building and managing CI/CD pipelines using Jenkins, GitHub Actions, GitLab CI, or ArgoCD.

● Observability & Monitoring: Experience using tools such as Datadog, Prometheus, Grafana, or Splunk for monitoring, logging, and alerting.

● Secrets & Security Management: Experience managing sensitive credentials using HashiCorp Vault, GCP Secret Manager, or Azure Key Vault.

● Architecture & Networking: Understanding of microservices architecture, service-oriented architecture, event-driven systems (Pub/Sub), and cloud networking principles.

Good to have skills:

● AI/ML Infrastructure: Familiarity with infrastructure for ML workloads such as Vertex AI, Azure Machine Learning, GPU node pools, or Vector Databases.

● Advanced Kubernetes: Working knowledge of Kyverno for policy management, Karpenter for cluster autoscaling, or building Kubernetes operators using Go.

● Multi-Cloud Management: Familiarity with Crossplane for managing multi-cloud environments and building cloud-native platforms.

● Cloud Reliability & FinOps: Understanding of disaster recovery, fault tolerance, and cost allocation practices through resource tagging.

● Domain & Compliance: Experience working in regulated environments such as BFSI or Insurance.

Join our core Platform Implementation Team to build intuitive, high-performance user interfaces for a cutting-edge enterprise Data & AI platform. You will develop scalable frontend applications including AI agent marketplaces, operational dashboards, and real-time chat interfaces that integrate seamlessly with backend APIs to support global business units and insurance advisors.

Key Responsibilities

• Design and develop responsive user interfaces for AI-driven applications such as web/app chat interfaces, AI copilots, and personalized content delivery systems.

• Build complex, data-rich dashboards supporting MLOps, GenAIOps, and AgentOps workflows to monitor model performance, manage approval gates, and track infrastructure costs.

• Develop a centralized Agent Marketplace portal for discovering, publishing, and managing reusable AI agents with strict versioning and access controls.

• Integrate frontend applications with APIs to consume reusable AI business services (e.g., document intelligence) and enterprise data products.

• Handle real-time data streams and asynchronous interactions to ensure smooth, low-latency user experiences for chat SDKs and streaming APIs.

• Implement robust state management solutions to support complex user workflows, session states, and multi-step AI agent interactions.

• Optimize frontend performance for high-traffic enterprise applications ensuring fast load times, smooth rendering, and cross-browser/device compatibility.

• Ensure all frontend implementations follow enterprise security standards, data privacy requirements, and WCAG accessibility guidelines relevant to the BFSI sector.

• Collaborate closely with UI/UX designers, backend engineers, integration engineers, and ML architects to bridge design and technical implementation.

• Write and maintain comprehensive unit and integration tests to ensure UI reliability and prevent regressions during continuous deployment cycles.

Must-Have Skills

• Experience with modern frontend frameworks such as React, Next.js, Angular, or Vue.js for building complex SPAs and SSR applications.

• Strong proficiency in JavaScript (ES6+) and TypeScript for writing scalable, maintainable, and type-safe code.

• Familiarity with modern styling approaches such as Tailwind CSS, SASS/LESS, Styled Components, Material UI, Ant Design, or Bootstrap.

• Experience building interfaces for AI/ML platforms, such as chatbot UIs, prompt engineering tools, or complex data visualization dashboards.

• Experience integrating frontend applications with REST APIs, GraphQL, WebSockets, and handling asynchronous data fetching using tools like React Query, SWR, or Axios.

• Familiarity with cloud-native deployment environments such as GCP Cloud Run, Firebase, or Azure Static Web Apps and build tools like Vite or Webpack.

• Hands-on experience with frontend testing frameworks including Jest, React Testing Library, Cypress, or Playwright.

• Experience integrating enterprise Identity and Access Management solutions such as OAuth 2.0, OIDC, or MSAL.

• Understanding of CI/CD pipelines for frontend applications and automated deployment workflows.

Good to Have

• Experience in the Life Insurance or broader BFSI domain with familiarity with user personas such as insurance agents, underwriters, or policyholders.

This role is responsible for architecting and implementing the Agentic capabilities of the PHI ecosystem. The engineer will lead the development of multi-agent systems, enabling seamless interoperability between AI agents, internal tools, and external services.

The position requires a strong focus on AI safety, secure agent orchestration, and tool-connected AI systems capable of executing complex workflows within the health insurance domain.

1. Agent Orchestration

• Build and manage autonomous AI agents using Agent Development Kit (ADK) and Vertex AI Agent Engine.
• Design and implement multi-agent workflows capable of handling complex tasks.

2. Interoperability

• Implement the Model Context Protocol (MCP) to enable connectivity between:
• AI agents
• Internal PHI tools
• External services and APIs.

3. Multimodal Development

• Build real-time, bidirectional audio applications using the Gemini Live API.
• Integrate image generation models and support multimodal AI capabilities.

4. Safety Engineering

• Implement AI safety layers to protect sensitive healthcare data.
• Use Model Armor and Cloud DLP API to:
• Sanitize prompts
• Prevent exposure of PII/PHI data
• Enforce secure AI interactions.

5. Agent-to-Agent (A2A) Communication

• Configure remote agent connectivity using the A2A SDK.
• Enable cross-agent collaboration and workflow orchestration.

Must-Have Skills

• Advanced proficiency with Agent Development Kit (ADK).
• Strong experience with Vertex AI Agent Engine.
• Hands-on experience with Model Context Protocol (MCP).
• Experience implementing Agent-to-Agent (A2A) workflows using the A2A SDK.
• Expertise in Google Gen AI SDK for Python.
• Experience building multimodal AI applications.
• Proven experience implementing AI safety layers, including:
• Model Armor
• Cloud DLP API

Good-to-Have Skills (Foundation)

Data & Analytics

• BigQuery optimization techniques, including:
• Partitioning
• Clustering
• Denormalization for performance and cost optimization.

Streaming & Real-Time Pipelines

• Experience building real-time data pipelines using:
• Google Pub/Sub
• BigQuery streaming pipelines