Data & AI Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop and enhance enterprise data and AI ecosystems.

• Create, build, and sustain scalable real-time streaming pipelines.

• Execute data and AI pipelines for structured, semi-structured, and unstructured data, facilitating AI and Agentic solutions.

• Prepare data utilizing extraction, chunking, embedding, and grounding techniques.

• Design data domains and products for reporting, data science, AI/ML, and analytics purposes.

• Architect and implement Retrieval-Augmented Generation (RAG) systems integrated with enterprise data infrastructure.

• Lead initiatives involving prototypes, experiments, and recommendations related to GenAI technologies.

• Model domain entities, relationships, and business logic using knowledge graphs.

• Integrate multi-source data with canonical representation and ensure semantic consistency.

• Develop and validate synthetic data workflows for agent evaluation purposes.

• Introduce AI-driven enhancements in data engineering productivity and automated data quality frameworks.

• Design scalable semantic layers and real-time analytics for conversational analytics.

• Connect semantic layers with AI/LLM platforms to enable secure, low-latency, context-rich data access.

• Monitor, alert, and manage incidents to maintain pipeline and system reliability, availability, and scalability.

• Implement redundancy, fault tolerance, and disaster recovery measures.

• Collaborate with DevOps and infrastructure teams for deployment, operation, and maintenance tasks.

• Mentor junior team members and lead communities of practice.

• Develop and optimize graph database queries, including Cypher and SPARQL.

• Design and implement GenAI solutions tailored for insurance-specific data applications.

• Collaborate with architects and stakeholders to realize the vision for AI and data pipelines.

⛳️ Requirements

• Over 6 years of hands-on experience in data engineering, building extensive, complex enterprise data ecosystems on cloud platforms (AWS, Azure, or GCP).

• Strong technical knowledge of Apache Kafka, AWS Kinesis, Spark Streaming, and distributed processing frameworks.

• Demonstrated experience with RAG architectures, vector search systems, chunking/embedding techniques, and LLM/Agentic AI data pipelines.

• Familiarity with graph databases such as Neo4j and Amazon Neptune.

• Proficiency in Cypher, SPARQL, or Gremlin.

• Strong expertise in domain-driven data design, dimensional modeling, semantic layer integration, and reusable data products.

• High proficiency in Python, Scala, or Java, in addition to SQL, DataOps, CI/CD, and containerized deployments.

• Experience dealing with complex, multi-structured data within the financial industry is strongly preferred.

• Exceptional leadership abilities, excellent stakeholder communication, and strong cross-functional collaboration skills.

• A proven track record of mentoring team members.

🏝️ Benefits

• Opportunity to engage with Fortune 500 companies and innovative market disruptors.

• Experience with cutting-edge technologies in artificial intelligence, machine learning, data, and cloud.

• Work alongside a dynamic and talented team.

• Chance to be part of an AI-first digital transformation and engineering company.

• Contract (C2C) engagement.