Quantiphi

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

Experience

• 5+ years of experience in building and managing Data Lakes, Data Warehouses, Data Integration, Data Migration, and Business Intelligence/Artificial Intelligence solutions on GCP, AWS, or Azure.
• Experience leading end-to-end cloud data engineering and migration projects.

Solution Design & Architecture

• Understand business requirements and translate them into functional and non-functional requirements.
• Design scalable, secure, high-performance, and resilient data platforms.
• Architect and implement end-to-end data pipelines for structured and unstructured data.
• Design distributed, batch, real-time, and event-driven data processing systems.
• Lead cloud migration initiatives from on-premises to GCP, AWS, or Azure.

Data Engineering & Big Data

• Hands-on experience with:
• Spark, PySpark, Scala
• Dataflow, Dataproc, EMR
• Hadoop ecosystem
• Expertise in ETL/ELT and data integration using:
• Informatica
• DataStage
• Talend
• OWB

Databases & Data Warehousing

• Experience with cloud and on-premises databases:
• BigQuery, Redshift
• Cloud SQL, Cloud Spanner, Bigtable
• RDS, Aurora, DynamoDB
• Oracle, Teradata, MySQL, DB2, SQL Server
• Exposure to NoSQL databases:
• MongoDB
• Cassandra
• CouchDB
• GraphDB

Data Pipeline & Integration Tools

Experience with one or more of:

• S3 / Cloud Storage
• Athena
• AWS Glue
• Sqoop
• Flume
• Hive
• Kafka
• Google Pub/Sub
• Amazon Kinesis
• Airflow / Cloud Composer
• Spark SQL
• Presto
• EMRFS

Cloud & Platform Knowledge

• Strong understanding of:
• GCP, AWS, and Azure services
• IaaS, PaaS, SaaS
• Containers & Microservices
• Cloud Security & Governance
• Ability to compare cloud-native services and recommend the best-fit architecture.

Business Intelligence & Machine Learning

• Experience with BI tools:
• Looker
• Tableau
• Power BI
• SAP BO
• Cognos
• Superset
• Good to have exposure to:
• TensorFlow
• PyTorch

Key Responsibilities

• Lead multiple cloud data engineering engagements across Data Lakes, Data Warehousing, Data Migration, and BI solutions.
• Work closely with business and technology stakeholders to gather requirements and define technical solutions.
• Own end-to-end project delivery, ensuring quality, timelines, and customer satisfaction.
• Build scalable, high-volume, batch and streaming data processing systems.
• Establish data validation, monitoring, and quality assurance processes.
• Ensure production data is reliable, accurate, and readily available for business users.
• Support pre-sales activities, including RFPs, RFIs, and solution design.
• Mentor junior engineers and drive technical excellence within the team.
• Contribute to reusable frameworks, accelerators, and best practices.

We're looking for a Senior Data Modeler to lead the design and evolution of our enterprise data warehouse, with a primary focus on transforming Guidewire (PolicyCenter, BillingCenter, ClaimCenter) and other insurance source system data into well-architected, analytics-ready models on Google BigQuery. This is a hands-on, high-autonomy role for someone who can own modeling decisions end-to-end — from source system analysis through to production-grade dimensional or Data Vault structures — with minimal oversight.

You'll work across multiple insurance lines of business (Property, Casualty, Financial Lines, Specialty) and partner closely with engineering, BI, and business stakeholders to ensure the data model is technically sound and supports reporting, actuarial, and analytics use cases.

Responsibilities:

• Design and own enterprise-scale data models across multiple insurance domains.
• Translate complex Guidewire schemas (PolicyCenter, BillingCenter, ClaimCenter) into governed warehouse structures.
• Apply appropriate modeling techniques including:
• Dimensional Modeling (Star/Snowflake)
• 3NF/Normalized Modeling
• Data Vault 2.0 (Hubs, Links, Satellites)
• Hybrid and Denormalized Models
• Model effective-dated and slowly changing insurance data, including policy versioning, endorsements, premium earning, and claims history.
• Design conformed dimensions and fact tables supporting Premium, Claims, and Billing reporting.
• Optimize BigQuery performance through partitioning, clustering, materialized views, query tuning, and cost-efficient schema design.
• Create and maintain ERDs, source-to-target mappings, data dictionaries, and lineage documentation.
• Collaborate with Data Engineers to ensure physical implementation aligns with logical models.
• Establish modeling standards and mentor team members through hands-on leadership.
• Independently drive solutions from ambiguous business requirements.
• Communicate modeling decisions and trade-offs to both technical and business stakeholders.

Required Skills & Experience:

• 7+ years of enterprise-scale Data Modeling experience.
• Strong Insurance domain expertise, preferably with Guidewire:
• PolicyCenter
• BillingCenter
• ClaimCenter
• Experience with alternative insurance platforms such as Duck Creek or Sapiens is also valuable.
• Deep expertise in:
• Dimensional Modeling (Kimball)
• 3NF Data Modeling
• Data Vault 2.0
• Denormalized Data Warehousing
• Strong BigQuery knowledge:
• Partitioning & Clustering
• Query Performance Optimization
• Cost-Aware Schema Design
• Expertise in modeling effective-dated and temporal data.
• Excellent communication and stakeholder management skills.
• Proven ability to lead through hands-on technical contributions.
• Ability to work independently with minimal supervision.

Nice to Have:

• Insurance premium calculations and reconciliation:
• Earned / Unearned / Written Premium
• GWP Reconciliation
• Premium Earning Schedules
• Knowledge of Guidewire data model patterns:
• Effective Dating
• FixedID / BranchID
• CDA / ODS Layers
• Experience with Data Vault automation tools.
• Exposure to Snowflake or Redshift.
• Experience mentoring Data Modelers and Data Engineers.

What Success Looks Like:

• Well-documented, scalable enterprise data models.
• Cost-efficient, high-performance modeling decisions.
• Improved modeling maturity across the team.
• Strong stakeholder confidence in the data architecture and design decisions.

We are looking for an experienced BI Solution Architect to lead the design and implementation of scalable, secure, and high-performing Data & BI platforms across Azure and GCP environments.

This role will own the end-to-end data architecture covering ingestion, storage, processing, transformation, analytics, and visualization while driving enterprise BI strategy, reporting governance, and modernization initiatives. The ideal candidate will work closely with business stakeholders, enterprise architects, and engineering teams to deliver robust, production-ready analytics solutions.

Key Responsibilities:

Data Architecture & Solution Design

• Define architecture for data ingestion, storage, processing, and transformation across Azure and GCP.
• Design scalable, resilient, and enterprise-grade data platforms aligned with architectural standards.
• Create data models, architecture blueprints, and end-to-end data flow designs.
• Collaborate with enterprise architects to ensure architectural compliance and governance.

Data Platform & Pipeline Architecture

• Design and oversee large-scale distributed data platforms and pipelines.
• Define standards for data ingestion, transformation, and consumption layers.
• Enable development of scalable and reusable data products.
• Promote modern data architectures including:
• Data Lake
• Lakehouse
• Data Mesh

BI Strategy & Architecture

• Define and drive enterprise BI strategy aligned with business objectives.
• Standardize BI architecture and reporting frameworks across the organization.
• Evaluate and recommend BI technologies including:
• Power BI
• Tableau
• SAP Business Objects (BO)
• Develop reusable blueprints, patterns, and accelerators.
• Lead proof-of-concepts (POCs) and BI modernization initiatives.

Visualization Governance

• Establish dashboard development standards and visualization best practices.
• Define governance frameworks to improve dashboard quality, consistency, and usability.
• Review dashboards prior to production deployment.
• Guide teams in creating intuitive, business-focused, and high-performance visualizations.

Data Governance & Enterprise Data Management

• Define and enforce enterprise data governance standards.
• Drive metadata management, master data management, and reference data practices.
• Establish data quality frameworks, validation mechanisms, and monitoring processes.
• Create technical glossaries and enterprise-wide data standards.

Security & Compliance

• Define access management and security standards across Data & BI platforms.
• Implement controls for sensitive and regulated data.
• Ensure compliance with enterprise policies and regulatory requirements.

Performance & Optimization

• Define monitoring, tuning, and optimization strategies for data pipelines and dashboards.
• Ensure scalability, responsiveness, and reliability of analytics platforms.
• Implement proactive monitoring and observability frameworks.

Leadership & Stakeholder Management

• Partner with Product Owners, Business Analysts, and stakeholders to translate requirements into architecture solutions.
• Collaborate with Data Engineers, Analysts, and Data Scientists to solve complex technical challenges.
• Mentor engineers and associate architects.
• Lead architecture reviews and drive solution governance.

Data Lifecycle & Operations

• Own end-to-end data lifecycle management:
• Ingestion
• Processing
• Storage
• Archival
• Ensure operational readiness and reliability of critical data platforms.
• Drive best practices around scalability, resilience, and sustainability.

Migration & Modernization

• Lead Tableau-to-Power BI migration strategy and execution.
• Design dual-platform architectures to ensure business continuity during migration.
• Drive legacy platform rationalization and modernization initiatives.
• Optimize infrastructure and licensing costs while reducing total cost of ownership (TCO).

Required Qualifications

Experience

• 8+ years of experience in Data Engineering, Big Data, Analytics, or Data Architecture.
• Proven experience designing enterprise-scale data platforms and analytics solutions.
• Strong stakeholder management and leadership experience.

Technical Expertise

• Strong expertise in Azure and GCP data ecosystems.
• Deep understanding of:
• Data Modeling
• ETL / ELT
• Distributed Systems
• Enterprise Data Platforms
• Experience with modern architectures:
• Data Lake
• Lakehouse
• Data Mesh

BI & Analytics

• Strong hands-on experience with:
• Power BI
• Tableau
• SAP Business Objects (BO)
• Expertise in:
• BI Architecture
• Dashboard Design
• Visualization Best Practices
• Reporting Governance

Data Governance

• Strong understanding of:
• Data Governance
• Data Quality
• Metadata Management
• Data Lifecycle Management

Must-Have Skills

• Data Architecture & Solution Design (Azure & GCP)
• Large-Scale Data Platform & Pipeline Design
• Data Modeling & ETL/ELT Architecture
• Distributed Systems
• Data Lake / Lakehouse / Data Mesh
• Power BI & Tableau
• Data Governance & Data Quality
• Enterprise BI Architecture
• Stakeholder Management & Leadership

Good-to-Have Skills

• Enterprise Data Platforms & Multi-Cloud Environments
• CI/CD Pipelines & Automation for Data & BI Platforms
• Real-Time / Streaming Data Architectures
• BI Transformation & Modernization Programs
• Tableau to Power BI Migration Experience
• Azure Data Engineer / Architect Certifications
• GCP Professional Data Engineer / Cloud Architect Certifications

Why Join Us?

• Lead architecture for enterprise-scale Data & BI platforms.
• Drive organization-wide data and analytics strategy.
• Work on modern cloud ecosystems across Azure and GCP.
• High ownership, visibility, and stakeholder exposure.
• Opportunity to shape long-term data, analytics, and visualization roadmaps.

Key Responsibilities

1. Target State Design

• Design and implement a scalable, secure, and domain-driven Data Lakehouse architecture on GCP.
• Define enterprise-grade data architecture standards and reference frameworks.

2. Migration Leadership

• Lead end-to-end migration from legacy platforms (Exasol, Lua, Control-M) to GCP BigQuery and Dataform.
• Drive migration planning, execution, validation, and optimization.

3. Data Modeling

• Design conceptual, logical, and physical data models.
• Implement:
• Data Vault 2.0 (Silver Layer)
• Dimensional Modeling (Gold Layer)

4. Framework Development

• Build reusable, configuration-driven data pipeline frameworks.
• Establish best practices for scalable and accelerated development.

5. Governance & Security

• Implement enterprise data governance using:
• Dataplex
• Data Catalog
• Analytics Hub
• Enable data lineage, cataloging, automated data quality checks, and PII masking using DLP.

6. Orchestration & Automation

• Architect workflows using Cloud Composer (Airflow).
• Automate infrastructure provisioning through Terraform (IaC).

7. BI Modernization

• Reconfigure and validate:
• ~1,000 Tableau & Power BI dashboards
• ~250 semantic/data models
• Ensure successful migration to BigQuery curated layers.

8. Stakeholder Management

• Partner with Engineering Leads, Architects, and Business Teams.
• Translate business requirements into Technical Design Documents (TDDs).
• Drive architecture reviews and approvals.

9. Quality Assurance

• Conduct code reviews and enforce enterprise standards.
• Support UAT and automated validation using QuerySurge.

10. Team Leadership

• Mentor data engineering teams.
• Conduct technical enablement sessions and knowledge transfer workshops.

Required Skills & Experience

Data Engineering & Database Expertise

• Strong expertise in BigQuery architecture, optimization, and stored procedures.
• Advanced SQL programming skills.
• Experience modernizing legacy ETL (Lua-based) into modern ELT frameworks.
• Strong understanding of:
• Data Vault 2.0
• Dimensional Modeling
• Experience with Exasol or similar legacy data warehouses is highly preferred.

GCP & Modern Data Stack

• Hands-on experience with:
• BigQuery
• Dataform (Mandatory)
• Cloud Composer (Airflow)
• Dataflow
• GCS
• Deep expertise in:
• Dataplex
• Data Catalog
• Analytics Hub

Security & Governance

• Experience implementing:
• IAM Roles & Policies
• Cloud KMS
• DLP API
• Strong understanding of enterprise governance and compliance frameworks.

Architecture & DevOps

• Enterprise Data Architecture design experience.
• Cost estimation, sizing, and capacity planning on GCP.
• CI/CD implementation and Terraform-based infrastructure automation.

Soft Skills

• Strong stakeholder management and communication skills.
• Experience leading discovery and solution design workshops.
• Ability to lead distributed Agile/Scrum teams.
• Excellent technical documentation skills (TDD/TSD creation).

Must-Have Skills

• 10+ years of Data Engineering / Data Architecture experience.
• Expertise in GCP BigQuery and Dataform.
• Hands-on experience with Data Vault 2.0.
• Experience implementing Dataplex for governance and security.
• Strong Terraform expertise.
• Proven experience migrating legacy warehouses such as:
• Exasol
• Teradata
• Netezza
• Similar enterprise DW platforms

Good-to-Have Skills

• Lua scripting experience.
• Control-M orchestration experience.
• Tableau and Power BI semantic/data model migration.
• QuerySurge for automated data validation.
• GCP Professional Data Engineer or Professional Cloud Architect certification.

Ideal Candidate Profile

• 10–15+ years of Data Engineering/Data Architecture experience.
• Led large-scale cloud modernization programs.
• Strong expertise in GCP Lakehouse architecture, governance, and migration frameworks.
• Comfortable working with enterprise stakeholders and leading cross-functional migration initiatives.

As a Senior Software Developer at Quantiphi, you will be responsible for designing, developing, and modernizing enterprise-grade applications using the Microsoft .NET ecosystem. You will work closely with stakeholders to gather requirements, build scalable and secure solutions, lead cloud migration initiatives, and mentor engineering teams while ensuring high standards of software quality and performance.

Key Responsibilities

Application Development & Architecture

• Design, develop, and maintain enterprise applications using C#, .NET Core, and ASP.NET Core.
• Build and maintain RESTful APIs using ASP.NET Core.
• Implement scalable, maintainable, and testable solutions following:
• Clean Architecture
• Hexagonal Architecture
• Domain-Driven Design (DDD)
• Apply design patterns including:
• Repository Pattern
• Factory Pattern
• Strategy Pattern
• CQRS
• Dependency Injection

Modernization & Cloud Migration

• Lead application modernization initiatives from on-premises environments to cloud platforms.
• Assess legacy .NET Framework applications and define migration strategies to .NET 6 / .NET 8.
• Transform monolithic applications into modular or microservices-based architectures.
• Conduct application discovery, dependency analysis, and proof-of-concept (POC) activities.
• Create migration roadmaps, rollback plans, and risk mitigation strategies.

Cloud & Containerization

• Design, deploy, and manage applications on AWS Cloud.
• Develop containerized solutions using Docker and Kubernetes.
• Build and maintain automated CI/CD pipelines.
• Implement cloud-native deployment and infrastructure automation practices.

Performance & Scalability

• Design highly scalable systems capable of handling millions of requests and high transaction volumes.
• Optimize application performance through:
• Caching
• Connection Pooling
• Asynchronous Processing
• Database Optimization
• Identify and resolve performance bottlenecks across application, database, and infrastructure layers.
• Implement distributed caching and scalable architectural patterns.

Security & Identity Management

• Implement authentication and authorization using:
• JWT
• OAuth2
• OpenID Connect
• Apply security best practices including:
• HTTPS
• Secure Headers
• Secret Management
• Input Validation
• Ensure compliance with enterprise security and governance standards.

DevOps & Observability

• Establish comprehensive testing strategies:
• Unit Testing
• Integration Testing
• Contract Testing
• End-to-End Testing
• Build and maintain CI/CD pipelines using GitHub Actions, Jenkins, or similar tools.
• Implement monitoring, logging, distributed tracing, and alerting solutions.
• Ensure application reliability, availability, and operational excellence.

Leadership & Collaboration

• Mentor and guide junior and mid-level developers.
• Drive technical decisions and architecture reviews.
• Collaborate with product owners, architects, business stakeholders, and engineering teams.
• Manage technical debt and promote engineering best practices.

Required Technical Skills

Languages & Frameworks

• C#
• .NET Framework 4.x
• .NET 6 / .NET 8
• ASP.NET MVC
• ASP.NET Core MVC

Data Technologies

• SQL Server
• Entity Framework Core
• LINQ
• ADO.NET

Cloud & DevOps

• AWS:
• EC2
• S3
• RDS
• DynamoDB
• ECS
• EKS
• Lambda
• IAM
• VPC
• CloudFormation
• CDK.NET
• AWS SDK for .NET
• Docker
• Kubernetes
• GitHub Actions
• Jenkins
• CI/CD Pipelines

Security

• JWT
• OAuth2
• OpenID Connect
• Identity Providers (IdPs)

Architecture & Design

• Clean Architecture
• Hexagonal Architecture
• CQRS
• Microservices
• Domain-Driven Design (DDD)
• Event-Driven Architecture

Preferred Experience

• 8+ years of software development experience.
• Hands-on experience in cloud migration and modernization projects.
• Experience building enterprise-scale distributed systems and microservices.
• Strong understanding of software architecture, security, and DevOps practices.
• Proven experience leading technical initiatives and mentoring development teams.

We are seeking a hands-on and technically strong Generative AI Engineer to join the AI Platform Capabilities team as part of a Platform Implementation Partner engagement. In this role, you will design, build, and deploy enterprise-grade Generative AI platform capabilities across multiple Local Business Units operating on both GCP and Azure environments.

The role focuses on engineering production-ready and reusable GenAI components across the full AI stack, including Decision & Orchestration Layers, Execution Runtime Layers, and Build & Lifecycle Layers. You will work closely with implementation partners and Data & AI teams to ensure scalable, enterprise-compliant AI capabilities are delivered within project timelines. This is a deeply technical engineering role with strong emphasis on implementation and operationalization rather than client management.

Key Responsibilities:

• Design and implement enterprise-grade RAG pipelines including ingestion, chunking, embeddings, vector search, retrieval logic, and evaluation frameworks.
• Build multi-agent AI systems with orchestration, semantic routing, memory management, workflow execution, and agent communication capabilities.
• Develop centralized LLM Gateway solutions covering model routing, observability, caching, rate limiting, and policy enforcement.
• Implement scalable GenAIOps, AgentOps, and MLOps frameworks for deployment, monitoring, evaluation, and governance of AI systems.
• Build scalable AI services and REST APIs using Python and deploy them on cloud-native infrastructure.
• Implement AI governance, safety guardrails, audit logging, PII protection, and compliance frameworks.
• Integrate AI solutions into CI/CD pipelines and provision infrastructure using Infrastructure-as-Code practices.

Must-Have Skills:

• Strong hands-on experience with Generative AI, RAG pipelines, embeddings, vector databases, and retrieval evaluation.
• Experience building agentic AI systems including orchestration frameworks, semantic routers, reasoning engines, and memory management.
• Expertise with LLM Gateway implementations, tool integrations, event-driven architectures, and AI runtime systems.
• Solid understanding of GenAIOps, AgentOps, and MLOps principles and tooling.
• Strong experience with GCP AI/ML ecosystem including Vertex AI, Cloud Run, GKE, Pub/Sub, BigQuery, and Cloud Storage.
• Excellent Python programming skills for AI pipelines, APIs, and automation workflows.
• Experience implementing AI governance, safety, compliance, and monitoring frameworks.
• Hands-on experience with CI/CD pipelines and Terraform-based infrastructure automation.

Nice-to-Have Skills:

• Experience working in multi-cloud environments across GCP and Azure.
• Familiarity with LangChain, LlamaIndex, RAGAS, DeepEval, or Vertex AI Rapid Eval.
• Exposure to Knowledge Graphs, Document Intelligence platforms, and Agent Marketplace concepts.
• Experience with enterprise AI-ready data layers including Vector Stores, Feature Stores, and Embedding Infrastructure.
• BFSI domain knowledge including regulatory compliance and data sovereignty considerations.
• Google Cloud Professional Machine Learning Engineer certification.
• Experience working in large-scale enterprise transformation programs. 

We are looking for a hands-on Associate / Architect – Generative AI to design, build, and deploy enterprise-grade GenAI platform capabilities across multiple business units. This role focuses on developing scalable and reusable AI components across the full stack, covering RAG systems, agent orchestration, LLM infrastructure, and GenAIOps on GCP (primary) and Azure.

Key Responsibilities

• Design and build production-ready Generative AI systems and platform components
• Develop and deploy scalable RAG pipelines including data ingestion, embeddings, retrieval, and APIs
• Build agentic AI systems with orchestration, routing, memory, and workflow management
• Develop and manage LLM infrastructure including model routing, caching, observability, and rate limiting
• Build scalable backend services and APIs for AI-driven applications
• Implement GenAIOps/MLOps practices including prompt management, evaluation, monitoring, and deployment
• Work extensively with GCP services such as Vertex AI, BigQuery, Cloud Run, GKE, and Pub/Sub
• Ensure AI governance, safety, compliance, PII protection, and auditability standards are maintained
• Design scalable enterprise AI architectures with strong focus on performance, reliability, and reusability
• Collaborate with cross-functional teams to deliver enterprise-grade AI solutions
• Mentor junior engineers and contribute to technical leadership, architecture discussions, and design reviews

Required Skills & Experience

• Strong hands-on experience building and deploying production-grade Generative AI and RAG systems
• Experience working on multi-agent or agentic AI architectures
• Strong proficiency in Python and backend/API development
• Hands-on experience with GCP AI/ML ecosystem including Vertex AI and BigQuery
• Solid understanding of LLM infrastructure, orchestration layers, and AI platform engineering
• Experience with CI/CD pipelines and Infrastructure as Code tools like Terraform
• Good understanding of GenAIOps/MLOps practices and model lifecycle management
• Strong system design and architecture experience for scalable AI platforms
• Exposure to enterprise application architecture and distributed systems
• Experience leading small engineering teams, mentoring developers, or owning technical delivery is preferred
• Understanding of AI safety, governance, and compliance best practices

Nice to Have

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

What We’re Looking For

• Engineers who have built and deployed real-world GenAI systems at scale
• Strong backend engineering and systems-thinking mindset
• Ability to thrive in fast-paced enterprise environments
• Ownership mindset with strong communication and collaboration skills 

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

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