Quantiphi

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.

Role & Responsibilities

• Design, develop, and deliver automation solutions to enhance platform functionality and reliability.
• Deploy, manage, and maintain Azure cloud infrastructure ensuring high availability, scalability, and security.
• Champion and implement Infrastructure as Code (IaC) practices using Terraform.
• Build and maintain containerized environments using Kubernetes (AKS).
• Develop self-service, self-healing, monitoring, and alerting systems for cloud platforms.
• Automate development, testing, and deployment workflows using CI/CD pipelines.
• Integrate DevOps tools such as Git, Jenkins/Azure DevOps, SonarQube, Artifactory, and Docker to streamline delivery pipelines.
• Ensure platform observability through monitoring, logging, and alerting frameworks.

Requirements

• 6+ years of experience in Cloud / DevOps / Platform Engineering roles.
• Strong hands-on experience with Microsoft Azure cloud infrastructure.
• Experience working with Azure services such as compute, networking, storage, identity, and messaging services.
• Expertise in container orchestration using Kubernetes (AKS).
• Strong experience implementing Infrastructure as Code using Terraform.
• Familiarity with cloud-native and microservices architecture patterns.
• Experience with relational and NoSQL databases such as PostgreSQL or Cassandra.

Additional Skills

• Strong Linux administration and troubleshooting skills.
• Programming or scripting experience in Bash, Python, Java, or similar languages.
• Hands-on experience with CI/CD tools such as Jenkins, Git, Maven, or Azure DevOps.
• Experience managing multi-region or high-availability cloud environments.
• Familiarity with Agile / Scrum / DevOps practices and collaboration tools.

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

We are seeking a Senior Machine Learning Engineer to support the development and deployment of advanced AI capabilities within the PHI ecosystem.

This role focuses on the execution of Generative AI tasks, including model integration and agent deployment. The candidate will be responsible for building RAG-based workflows and ensuring AI interactions remain grounded and accurate using Google Cloud AI tools.

Key Responsibilities

1. GenAI Integration

• Develop and maintain integrations with Gemini 1.5 Pro and Flash models
• Use the Google Gen AI SDK for Python to build and manage model integrations

2. Agent Deployment

• Assist in deploying AI agents to Vertex AI Agent Engine
• Work with the Agent Development Kit (ADK) for agent lifecycle management

3. RAG & Embeddings

• Generate and manage text and multimodal embeddings
• Support semantic search and Retrieval-Augmented Generation (RAG) pipelines

4. Testing & Quality

• Run evaluation scripts to verify model output quality
• Ensure models follow grounding and response accuracy guidelines

Must-Have Skills

• Strong Python programming
• Experience working with REST APIs
• Hands-on experience with Vertex AI Studio
• Experience working with Gemini APIs
• Understanding of Agentic AI concepts
• Familiarity with ADK CLI
• Experience or understanding of RAG architecture
• Knowledge of embedding generation

Good-to-Have Skills (Foundation):

BigQuery

• Basic SQL knowledge
• Experience with data loading
• Ability to debug and troubleshoot queries

Data Streaming

• Familiarity with Google Pub/Sub
• Understanding of synthetic data generation

Visualization

• Basic reporting and dashboards using Looker Studio

As a Backend Engineer, you will be a core member of the Platform Implementation Team, responsible for building the robust, scalable, and secure backend infrastructure for a multi-cloud enterprise Data & AI platform.

You will design and develop high-performance microservices, RESTful APIs, and event-driven architectures that serve as the backbone for enterprise-wide applications.

Working closely with Platform Engineers, Data Modelers, and UI teams, you will ensure seamless data flow between core business systems (CRM, ERP) and the platform, enabling the rollout of critical business services across multiple global Local Business Units (LBUs).

Backend Development

• Design and develop scalable backend services and microservices
• Build and maintain RESTful APIs for enterprise applications
• Define and maintain API contracts using OpenAPI/Swagger

Platform & System Integration

• Enable seamless integration between enterprise systems (CRM, ERP) and the platform
• Support data flow across multiple global business units

Event-Driven Architecture

• Implement asynchronous processing and event-driven systems
• Work with message brokers and streaming platforms

Cross-Functional Collaboration

• Collaborate with platform engineers, data modelers, and frontend teams
• Contribute to architecture discussions and backend design decisions

Must-Have Skills

Experience

• 5–7 years of hands-on experience in backend software engineering
• Experience building enterprise-grade backend systems

Core Programming

Strong proficiency in at least one backend language:

• Python
• Node.js
• Java

Strong understanding of:

• Object-oriented programming (OOP)
• Functional programming principles

API & Microservices

• Extensive experience building RESTful APIs
• Experience designing microservices architectures
• Ability to define API contracts using OpenAPI / Swagger

Cloud Infrastructure

Hands-on experience with cloud platforms:

• Google Cloud Platform (GCP)
• Microsoft Azure

Examples of services:

• Cloud Functions
• Cloud Run
• Azure App Services

Database Management

Experience with both Relational and NoSQL databases

Relational:

• PostgreSQL
• Cloud SQL

NoSQL:

• Schema design
• Complex querying
• Performance optimization

Event-Driven Architecture

Experience with asynchronous processing and message brokers:

• GCP Pub/Sub
• Apache Kafka
• RabbitMQ

Security & Authentication

Strong understanding of:

• OAuth 2.0
• JWT authentication
• Role-Based Access Control (RBAC)
• Data encryption

Software Engineering Best Practices

• Writing clean, maintainable code
• Version control using Git
• Writing unit and integration tests
• Familiarity with CI/CD pipelines
• Containerization using Docker

Good-to-Have Skills

AI & LLM Integration

• Experience integrating Generative AI models
• Exposure to:
• OpenAI
• Vertex AI
• LLM gateways
• Retrieval-Augmented Generation (RAG)

Frontend Exposure

Basic familiarity with frontend frameworks such as:

• React
• Next.js
• Angular

Understanding how backend APIs integrate with UI applications

Advanced Data Stores

Experience with:

• Vector databases (Pinecone, Milvus)
• Knowledge graphs

Domain Knowledge

• Experience in Life Insurance or BFSI sector
• Understanding of enterprise data governance and compliance standards

We are looking for a highly experienced and technically strong AI Engineering Manager to lead and mentor a team of Machine Learning and AI Engineers. This role will drive the execution, delivery, and operational excellence of enterprise AI/ML products and platforms within the Life Insurance and Financial Services sector.

The role operates primarily in a GCP cloud environment and requires bridging architectural design with hands-on engineering execution. The candidate will manage engineering workloads, guide technical decisions, ensure high code quality, and drive project timelines for enterprise-grade AI solutions.

Key Responsibilities

Team Leadership & Project Management

• Own end-to-end delivery of AI/ML projects including sprint planning, backlog grooming, workload management, and resource allocation.
• Provide technical guidance and mentorship to AI/ML engineers, including code reviews and best practices for MLOps, software engineering, and cloud infrastructure.
• Act as the primary technical point of contact for product managers, architects, and business stakeholders.
• Define and enforce engineering standards for development, testing, CI/CD, and monitoring of AI services.
• Recruit, onboard, mentor, and conduct performance reviews for the AI engineering team.
• Collaborate with Data Engineering, DevOps, and other teams to integrate AI models into enterprise systems and data pipelines.
• Identify and manage technical risks, dependencies, and delivery blockers to maintain project velocity.

Technical Delivery

• Implement and operationalize MLOps pipelines for model training, versioning, deployment, monitoring, and explainability.
• Guide teams in leveraging AI platform capabilities such as RAG pipelines, LLM gateways, and vector databases to build business use cases.
• Ensure security, scalability, and performance of production AI services and underlying cloud infrastructure.

Must-Have Skills & Requirements

• 8+ years of experience in Software Engineering, Machine Learning Engineering, or Data Science.
• Minimum 3+ years in a team management or leadership role.
• Proven experience managing and mentoring engineering teams.
• Strong experience with project management methodologies (Scrum/Kanban) and tools such as Jira.
• Hands-on experience with production-scale MLOps and GenAIOps implementations.
• Deep expertise in cloud platforms, preferably GCP.
• Strong understanding of modern data architecture including vector databases, data warehousing, and ETL/ELT pipelines.
• Solid software engineering fundamentals including API design, system architecture, Git, and CI/CD or DevSecOps pipelines.
• Experience with GenAI technologies such as LLM orchestration frameworks, prompt engineering, and RAG architectures.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

Good-to-Have / Preferred Skills

• Experience managing distributed or multi-geographical engineering teams.
• Knowledge of regulatory requirements within the BFSI or insurance domain (data privacy, Responsible AI).
• Azure cloud or multi-cloud project experience.
• Experience with streaming data platforms and real-time AI processing.
• Cloud certifications such as GCP Professional Cloud Architect or ML Engineer.

We are seeking a highly skilled Senior Backend Developer with deep expertise in Python and FastAPI to join our team. This role focuses on building high-performance, scalable backend services capable of handling high request volumes while integrating advanced LLM technologies.

The ideal candidate will design robust distributed systems, implement efficient data storage solutions, and ensure enterprise-grade security within an Azure-based infrastructure. This is a great opportunity to work on AI/ML integrations and mission-critical applications requiring high performance and reliability.

Key Responsibilities:

Backend Development

• Design and maintain high-performance backend services using Python and FastAPI
• Implement advanced FastAPI features such as dependency injection, middleware, and async programming
• Write comprehensive unit tests using pytest
• Design and maintain Pydantic schemas

High-Concurrency Systems

• Implement asynchronous code for high-volume request processing
• Apply concurrency patterns and atomic operations to ensure efficient system performance

Data & Storage

• Optimize MongoDB operations
• Implement Redis caching strategies (TTL, performance tuning, caching patterns)

Distributed Systems

• Implement rate limiting, retry logic, failover mechanisms, and region routing
• Build microservices and event-driven architectures
• Work with EventHub, Blob Storage, and Databricks

AI/ML Integration

• Integrate OpenAI API, Gemini API, and Claude API
• Manage LLM integrations using LiteLLM
• Optimize AI service usage within the Azure ecosystem

Security

• Implement JWT authentication
• Manage API keys and encryption protocols
• Implement PII masking and data security mechanisms

Collaboration

• Work with cross-functional teams on architecture and system design
• Contribute to engineering best practices and technical improvements
• Mentor junior developers where required

Must-Have Skills & Requirements

Experience

• 7+ years of hands-on Python backend development
• Bachelor’s degree in Computer Science, Engineering, or related field
• Experience building high-traffic, scalable systems

Core Technical Skills

Python

• Advanced knowledge of asynchronous programming, concurrency, and atomic operations

FastAPI

• Expert-level experience with dependency injection, middleware, and async code

Testing

• Strong experience with pytest and Pydantic schemas

Databases

• Hands-on experience with MongoDB and Redis
• Strong understanding of caching patterns, TTL, and performance optimization

Distributed Systems

• Experience with rate limiting, retry logic, failover mechanisms, high concurrency processing, and region routing

Microservices

• Experience building microservices and event-driven systems
• Exposure to EventHub, Blob Storage, and Databricks

Cloud

• Strong experience working in Azure environments

AI Integration

• Familiarity with OpenAI API, Gemini API, Claude API, and LiteLLM

Security

• Implementation experience with JWT authentication, API keys, encryption, and PII masking

Soft Skills

• Strong problem-solving and debugging skills
• Excellent communication and collaboration
• Ability to manage multiple priorities
• Detail-oriented approach to code quality
• Experience mentoring junior developers

Good-to-Have Skills

Containerization

• Docker, Kubernetes (preferably within Azure)

DevOps

• CI/CD pipelines and automated deployment

Monitoring & Observability

• Experience with Grafana, distributed tracing, custom metrics

Industry Experience

• Experience in Insurance, Financial Services, or regulated industries

Advanced AI/ML

• Vector databases
• Similarity search optimization
• LangChain / LangSmith

Data Processing

• Real-time data processing and event streaming

Database Expertise

• PostgreSQL with vector extensions
• Advanced Redis clustering

Multi-Cloud

• Experience with AWS or GCP alongside Azure

Performance Optimization

• Advanced caching strategies
• Backend performance tuning

Role & Responsibilities

• Develop and deliver automation software to build and improve platform functionality
• Ensure reliability, availability, and manageability of applications and cloud platforms
• Champion adoption of Infrastructure as Code (IaC) practices
• Design and build self-service, self-healing, monitoring, and alerting platforms
• Automate development and testing workflows through CI/CD pipelines (Git, Jenkins, SonarQube, Artifactory, Docker containers)
• Build and manage container hosting platforms using Kubernetes

Requirements

• Strong experience deploying and maintaining GCP cloud infrastructure
• Well-versed in service-oriented and cloud-based architecture design patterns
• Knowledge of cloud services including compute, storage, networking, messaging, and automation tools (e.g., CloudFormation/Terraform equivalents)
• Experience with relational and NoSQL databases (Postgres, Cassandra)
• Hands-on experience with automation/configuration tools (Puppet, Chef, Ansible, Terraform)

Additional Skills

• Strong Linux system administration and troubleshooting skills
• Programming/scripting exposure (Bash, Python, Core Java, or Scala)
• CI/CD pipeline experience (Jenkins, Git, Maven, etc.)
• Experience integrating solutions in multi-region environments
• Familiarity with Agile/Scrum/DevOps methodologies

Build, deploy, and maintain production-grade AI/ML solutions for Fortune 500 enterprise clients on Google Cloud Platform. Hands-on role focused on shipping scalable AI systems across GenAI, agentic workflows, traditional ML, and computer vision.

Key Responsibilities:

Generative AI & Agentic Systems

• Design and build GenAI applications (RAG, agentic workflows, multi-agent systems)
• Develop intelligent systems with memory, planning, and reasoning capabilities
• Implement prompt engineering, context optimization, and evaluation frameworks
• Build observable and reliable multi-agent architectures

Traditional ML & Computer Vision

• Develop ML pipelines (forecasting, recommendation, classification, regression)
• Build production-grade computer vision solutions (document AI, image analysis)
• Perform feature engineering, model optimization, and benchmarking

MLOps & Production Engineering

• Own end-to-end ML lifecycle (CI/CD, testing, versioning, deployment)
• Build scalable APIs, microservices, and data pipelines
• Monitor models, detect drift, and implement A/B testing frameworks

Knowledge Solutions

• Architect knowledge graphs and semantic search systems
• Implement hybrid retrieval (vector + keyword search)

Client Collaboration

• Present technical solutions to enterprise clients
• Collaborate with architects, data engineers, and business teams

Required Skills & Experience

• 3–6 years of hands-on ML Engineering experience
• Strong Python and software engineering fundamentals
• Experience shipping production ML systems on cloud (GCP preferred)
• Experience across GenAI, Traditional ML, Computer Vision
• MLOps experience and RAG-based systems

Preferred

• GCP Professional ML Engineer certification
• Knowledge graphs / semantic search experience
• Experience in regulated industries (Healthcare / BFSI)
• Open-source or technical publications