Quantiphi
https://quantiphi.comAbout
Quantiphi is an award-winning AI-first digital engineering company driven by the desire to reimagine and realize transformational opportunities at the heart of the business. Since its inception in 2013, Quantiphi has solved the toughest and most complex business problems by combining deep industry experience, disciplined cloud, and data-engineering practices, and cutting-edge artificial intelligence research to achieve accelerated and quantifiable business results.
Tech stack
NodeJS (Node.js)
PythonCompany video
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Bengaluru, Mumbai, and Trivandrum
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Jobs at Quantiphi
KubernetesRole & 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
Machine Learning (ML)We are hiring a hybrid Technical Project Manager and Data Product Manager to drive enterprise AI strategy implementation in the Life Insurance / Financial Services domain. This role owns the AI/data platform roadmap, data products, and delivery execution, while acting as the bridge between business stakeholders and engineering teams.
Key Responsibilities
- Lead requirement gathering and client workshops to define AI use cases and data products
- Own product vision and roadmap aligned with enterprise AI strategy
- Drive release planning, sprint execution, and global rollouts
- Collaborate with ML, GenAI, and Data Engineering teams for implementation
- Manage cross-team coordination across platforms and external partners
- Deliver executive reporting on progress, risks, and ROI
- Ensure stakeholder alignment across business units and leadership
- Proactively manage risks, dependencies, and execution challenges
Must-Have Skills & Experience
- 8+ years in Technical Project Management/Product roles in Data, AI, or Analytics
- Strong client-facing/consulting experience (workshops, solutioning, RFPs)
- Good understanding of AI/ML lifecycle, GenAI (LLMs, RAG), MLOps, and Azure/GCP
- Strong Agile/Scrum experience with tools like Jira, Confluence
- Experience managing complex enterprise releases
- Excellent communication and stakeholder management skills
- Ability to translate technical concepts into business outcomes
Good-to-Have
- PMP / CSM / PMI-ACP certifications
- Experience with PaaS platforms or API-based services
- BFSI / Life Insurance domain exposure
- Understanding of data governance and regulatory environments
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.
PythonWe 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 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
.NET
Amazon Web Services (AWS)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
.NETAmazon Web Services (AWS)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
NodeJS (Node.js)PythonResponsible 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
PythonWe 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
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🧑🦰Founding Team
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