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Heitmeyer Consulting has a banking client that has a need within their Chief Data Office for a pioneering Lead AI Data Platform Engineer to establish and mature our Artificial Intelligence capabilities from a data infrastructure perspective. This is a strategic role focused on building robust, scalable platforms that accelerate AI application development and deployment. The ideal candidate will bridge the gap between data engineering, ML Ops, and applied science, focusing heavily on modern AWS AI services (Bedrock), Retrieval-Augmented Generation (RAG) frameworks, and Vector Databases to automate complex manual processes and solve real-world problems. Role must be based in Dallas, TX or Tulsa, OK.
Top Required Skills:
- Require extensive experience in Data Engineering, ML Ops, or AI Platform Architecture roles, with leadership experience preferred.
- Strong Cloud AI expertise with deep, hands-on experience with AWS services, specifically AWS Bedrock, Sagemaker, and related data services (S3, Lambda, EC2).
- Strong understanding of machine learning operations, generative AI principles, LLMs, and practical experience implementing RAG patterns.
- Practical experience deploying and managing vector databases and creating efficient vector embeddings.
- Experience defining and deploying appropriate compute resources (MCP servers, GPU instances) within a cloud environment to meet ML workload demands.
- Proficiency in Python (essential for ML/AI tasks), infrastructure-as-code tools (Terraform/CloudFormation), and ML Ops platforms/frameworks.
- Demonstrated ability to take a proactive approach to identifying opportunities where emerging AI technologies can solve complex business problems.
Nice-to-have:
- Background within financial services would be preferred but not required.
Key Responsibilities
- Define the technical roadmap for the AI data platform, selecting and integrating the necessary tools and services to enable scalable AI/ML solutions.
- Lead the design and implementation of Retrieval-Augmented Generation (RAG) frameworks using AWS Bedrock, ensuring efficient data retrieval and integration with Large Language Models (LLMs) for enterprise use cases.
- Architect, deploy, and manage vector databases/vector embeddings stores (e.g., Pinecone, Weaviate, Pgvector on RDS, or AWS OpenSearch) to support semantic search and RAG architectures.
- Establish and implement ML Ops best practices for AI workloads, managing the full lifecycle of AI applications from experimentation to production deployment, monitoring, and iteration.
- Manage the specification and build-out of necessary compute infrastructure, including high-performance GPU/CPU Multi-Core Processing (MCP) servers on AWS (e.g., EC2 instances with specific accelerators) required for fine-tuning models or handling inference loads.
- Identify manual business processes suitable for automation via AI solutions and lead the technical execution using AWS Bedrock and related services.
- Define data governance and security protocols specifically for AI data pipelines, vector stores, and model outputs, ensuring responsible and compliant AI use.
- Serve as the technical lead and liaison between data science teams, software engineering, and cloud infrastructure teams.
Heitmeyer Consulting is an equal opportunity employer, and we encourage all qualified candidates to apply. Qualified applicants will be considered without regard to minority status, gender, disability, veteran status or any other characteristic protected by law.