Lead AI/ML Data Platform Engineer

Core Specialty; Cincinnati (OH); Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Medical, dental, vision, and life ins.

Disability insurance

401(k) company-match

Employee Assistance Program

Health Savings Account

Flexible Spending Account

Wellness program

Professional development

Job summary

Core Specialty is seeking a Lead AI/ML Data Engineering professional in Cincinnati, OH to drive AI/ML enablement across the organization. You will design and optimize data pipelines, own end-to-end implementations, and define scalable AI infrastructure patterns while collaborating with Data Governance and Enterprise Architecture.

You will lead the definition of AI/ML frameworks, evaluate tools such as LangChain and vector databases, and build prototypes with production-grade practices.

Qualifications

• Data pipeline design, optimization, and distributed processing (Spark, dbt, Airflow, Kafka, etc.)
• Hands-on with Snowflake, Databricks, and/or Azure Synapse Analytics; architect/optimize workloads
• Strong knowledge of AWS, Azure, or GCP and modern data warehouse architectures
• Strong Python skills; production-grade software engineering practices
• Experience building ML/AI systems via orchestration, tool-calling, retrieval systems
• Practical experience with LLM APIs (OpenAI) and open-weight models
• Prompt engineering and evaluation as established discipline
• Understanding of context windows, tokenization, embeddings, latency and cost tradeoffs
• Experience with vector databases and embedding models
• Chunking strategies, hybrid search, and reranking
• Agent frameworks (LangChain, LangGraph, LlamaIndex)
• Function-calling design and multi-step reasoning
• Model optimization: fine-tune, prompt, or RAG
• Familiarity with LoRA and parameter-efficient methods
• MLOps/LLMOps practices and governance awareness
• Model evaluation, A/B testing, observability, tracing/logging of model calls
• Latency/cost optimization in deployment; caching and streaming responses
• Versioning prompts and models; governance of safety and PIIs

Responsibilities

• Design, build, and optimize data pipelines and platform components for analytics and AI/ML use cases
• Own complex initiatives from design through rollout with minimal oversight
• Resolve performance, scalability, reliability issues across data platform
• Proactively propose improvements and fill gaps in data engineering capabilities
• Produce clean, well-tested code and infrastructure-as-code with engineering hygiene
• Define AI/ML frameworks; evaluate tools, platforms, and standards
• Build working AI/ML prototypes delivering immediate value
• Shape MLOps strategy including deployment, monitoring, versioning, lifecycle management
• Collaborate with Data Governance to ensure alignment with governance and compliance
• Advocate scalable AI infrastructure patterns (feature stores, governed datasets, streaming for training/inference)
• Partner with Data Science/Engineering/business to assess readiness gaps and roadmap
• Serve as SME and thought partner on emerging AI/ML technologies and industry trends
• Document AI/ML standards and decisions for consistent adoption
• Provide architectural guidance and best practices for AI readiness and ML Ops
• Collaborate with Enterprise Architecture on architectural blueprints for AI readiness
• Other duties as assigned

Skills

Data engineering

Data platforms

Cloud

Python

API integration

LLM experience

Prompt engineering

Model fundamentals

Vector search

Search techniques

Agent orchestration

Tool use/agents

Model adaptation

LoRA

MLOps/LLMOps

Evaluation/observability

Deployment patterns

Versioning

Safety/governance

Education

Bachelor's degree

Tools

Snowflake

Databricks

Azure Synapse Analytics

AWS

Azure

GCP

Pinecone

Weaviate

pgvector

LangChain

LangGraph

LlamaIndex

LoRA

MLOps/LLMOps

Data Vault 2.0

Ensemble data modeling

Job description

Core Specialty is seeking a Lead AI/ML Data Engineering professional in Cincinnati, OH to drive AI/ML enablement across the organization. You will design and optimize data pipelines, own end-to-end implementations, and define scalable AI infrastructure patterns while collaborating with Data Governance and Enterprise Architecture.

You will lead the definition of AI/ML frameworks, evaluate tools such as LangChain and vector databases, and build prototypes with production-grade practices.