Note: The job is a remote job and is open to candidates in USA. Quantiphi is an AI-first digital engineering and consulting company focused on delivering intelligent, scalable, and transformative AI solutions. The Senior Data & AI Engineer will build and modernize enterprise data and AI ecosystems, including real-time streaming pipelines, data products, semantic layers, knowledge graphs, and GenAI/Agentic data infrastructure. The role also involves designing RAG architectures, automated data quality systems, and scalable AI-driven data engineering solutions.
Responsibilities
- Senior Data and AI Engineering professional for large and complex data ecosystem leveraging data domains, data products, cloud and modern technology stack
- Real-Time Data Streaming: Design, build and maintain scalable and robust real-time data streaming pipelines using technologies such as Apache Kafka, AWS Kinesis, Spark streaming, or similar
- Senior Data and AI Engineering professional responsible for Implementing Data and AI pipelines that bring together structured, semi-structured and unstructured data to support AI and Agentic solutions. This Includes pre-processing with extraction, chunking, embedding and grounding strategies to get the data ready
- Design and Develop Data and AI-driven systems to improve data capabilities, ensuring compliance with industry best practices
- Design and Develop data domains and data products for various consumption archetypes including Reporting, Data Science, AI/ML, Analytics etc
- Design and Implement efficient Retrieval-Augmented Generation (RAG) architectures and integrate with enterprise data infrastructure
- Collaborate with cross-functional teams to integrate solutions into operational processes and systems supporting various functions
- Stay up to date with industry advancements in GenAI and apply modern technologies and methodologies to our systems. This includes leading prototypes (POCs), conducting experiments, and recommending innovative tools and technologies to enhance data capabilities enabling business strategy
- Model domain entities, relationships, and business logic in knowledge graphs (e.g., Neo4j, Amazon Neptune, RDF). Integrate data from multiple sources, ensuring canonical representation and semantic consistency
- Synthetic data generation: Develop and validate synthetic data to simulate rare events and edge cases, supporting robust agent evaluation. Integrate synthetic data workflows with automated testing frameworks to ensure consistent, scalable agent performance assessment
- Identify and Champion AI driven Data Engineering productivity improvements capabilities accelerating end-to-end data delivery lifecycle. This includes researching and implementing innovative solutions such as AI-driven auto-generation of data pipelines, advanced DevOps practices (AI augmented self-healing data pipelines) for data and automated data quality frameworks
- Semantic layer and Real time analytics: Design and implement scalable semantic layer with dynamic query translation to deliver real time insights for conversational analytics
- Integrate the semantic layers with AI/LLM platforms to provide low-latency, secure, and context-rich data access, optimized for high concurrency and aligned with enterprise governance standards
- Ensure the reliability, availability, and scalability of data pipelines and systems through effective monitoring, alerting, and incident management
- Implement best practices in reliability engineering, including redundancy, fault tolerance, and disaster recovery strategies
- Collaborate closely with DevOps and infrastructure teams to ensure seamless deployment, operation, and maintenance of data systems
- Mentoring junior team members and leading communities of practice to deliver high-quality data and AI solutions while promoting best practices, standards, and adoption of reusable patterns
- Design and Develop graph database solutions for complex data relationships supporting AI systems, this also includes developing and optimizing queries (e.g., Cyhper, SPARQL) to enable complex reasoning, relationship discovery, and contextual enrichment for AI agents
- Design and Apply GenAI solutions to insurance-specific data use cases and challenges
- Partner with architects and stakeholders to influence and implement the vision of the AI and data pipelines while safeguarding the integrity and scalability of the environment
Skills
- 6+ years
- 6+ years of hands-on data engineering experience building large-scale, complex enterprise data ecosystems on cloud platforms (AWS, Azure, or Google Cloud Platform)
- Deep technical expertise in streaming platforms (Apache Kafka, AWS Kinesis, Spark Streaming) and distributed processing frameworks
- Proven track record in RAG architectures, vector search systems, chunking/embedding techniques, and data pipelines built specifically for LLM and Agentic AI consumption
- Experience with graph databases (Neo4j, Amazon Neptune) and query languages (Cypher, SPARQL, or Gremlin)
- Proficiency in domain-driven data design, dimensional modeling, semantic layer integration, and building reusable data products
- High proficiency in Python, Scala, or Java, alongside SQL, DataOps, CI/CD, and containerized deployments
- **Soft Skills:** Exceptional leadership, stakeholder communication, and cross-functional collaboration skills with a track record of mentoring team members
- Experience in the Financial Industry handling complex, multi-structured domain data is strongly preferred
Benefits
- Be part of the fastest-growing AI-first digital transformation and engineering company in the world
- Be a part of an energetic team of highly dynamic and talented individuals
- Exposure to working with fortune 500 companies and innovative market disruptors
- Exposure to the latest technologies related to artificial intelligence and machine learning, data and cloud
- Work Location: USA - Remote
Company Overview
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