Data & AI Engineer

SolomonEdwards is a national professional services firm offering financial, operational and technology consulting and operations support. Whether you need specialized consulting services or additional support to execute key initiatives, we bring the right people and expertise together to turn business challenges into value-creation opportunities. About the Role Our AI Data Engineer builds and maintains the data infrastructure, pipelines, and semantic layers that feed enterprise AI applications and machine learning models. They bridge the gap between software development, data science, and AI by ensuring machine learning models have secure, scalable, and high-quality data. Responsibilities Pipeline Development: Design, build, and maintain scalable ETL/ELT pipelines for extracting, cleaning, and processing structured and unstructured enterprise data. AI/ML Integration: Build foundations for Large Language Models (LLMs) and Generative AI applications, including retrieval-augmented generation (RAG) and semantic layers. Vector & Graph Databases: Implement and optimize vector indexes, knowledge graphs, and hybrid retrieval systems to enhance AI reasoning and response quality. Data Quality & Governance: Establish validation checks, bias detection, and data lineage tracking to ensure model accuracy and regulatory compliance (e.g., SOC 2, HIPAA). MLOps & LLMOps: Deploy models into production, automate infrastructure, and monitor model performance, telemetry, and drift over time. Qualifications Programming: Advanced proficiency in Python and SQL; familiarity with general-purpose languages like Java or TypeScript. Data Tools: Experience with distributed data processing and workflow orchestration (e.g., Apache Spark, Airflow, Dagster, dbt). Cloud AI Stacks: Hands‑on experience with cloud AI and data platforms (e.g., AWS Bedrock, Google Vertex AI, Azure OpenAI). Search & Vector Stores: Proficiency in vector and search technologies like Pinecone, Elasticsearch/OpenSearch, Milvus, or FAISS. Knowledge Graphs (Preferred): Experience with graph databases (e.g., Neo4j, AWS Neptune) and query languages. Preferred Skills Data Modeling Skills Database Design Distributed Computing Parallel Processing Algorithm Design Statistical Analysis Mathematical Modeling Probability Theory Statistics Programming Skills Software Engineering System Design Scalability Engineering Performance Optimization Code Optimization Testing Version Control Git for Data/ML Pay range and compensation package Data & AI Engineering offers competitive compensation packages. Equal Opportunity Statement We are committed to diversity and inclusivity in our hiring practices. #J-18808-Ljbffr