Data & AI Engineer

Quantiphi

4d • Remote

About The Position

Quantiphi is an award-winning, AI-First digital engineering and consulting company focused on delivering high-impact Services and Solutions that help organizations solve what truly matters. We partner with enterprises to reimagine their businesses through intelligent, scalable, and transformative AI driving measurable outcomes at the very core of their operations. Since our founding in 2013, Quantiphi has tackled some of the world’s most complex business challenges by combining deep industry expertise, disciplined cloud and data engineering practices, and cutting-edge applied AI research. Our work is rooted in delivering accelerated, quantifiable business value, not just technology for technology’s sake. Headquartered in Boston, Quantiphi is a global organization with 4,000+ professionals serving clients across key industry verticals, including BFSI, Healthcare & Life Sciences, CPG, MFG, TME etc. As an Elite and Premier partner to leading cloud and AI platforms such as NVIDIA, Google Cloud, AWS, and Snowflake, we build and deliver enterprise-grade AI services and solutions that create real-world impact. We are seeking an experienced Senior Data & AI Engineer to join our team. In this role, you will be a key driver in building and modernizing our enterprise data and AI ecosystem. You will architect and deploy scalable real-time streaming pipelines, modern data products, semantic layers, knowledge graphs, and GenAI/Agentic data infrastructure. The ideal candidate blends deep expertise in large-scale data engineering with cutting-edge hands-on skills in AI engineering, RAG architectures, and automated data quality systems to power next-generation business capabilities.

Requirements

• 6+ years of hands-on data engineering experience building large-scale, complex enterprise data ecosystems on cloud platforms (AWS, Azure, or GCP).
• 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.
• Experience in the Financial Industry handling complex, multi-structured domain data is strongly preferred.
• Exceptional leadership, stakeholder communication, and cross-functional collaboration skills with a track record of mentoring team members.

Responsibilities

• Senior Data and AI Engineering professional for large and complex data ecosystem leveraging data domains, data products, cloud and modern technology stack
• Design, build and maintain scalable and robust real-time data streaming pipelines using technologies such as Apache Kafka, AWS Kinesis, Spark streaming, or similar.
• 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.
• 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.
• 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.

Benefits

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed