Senior AI Engineer – Claude & Databricks

Techstra Solutions•Pittsburgh, PA

About The Position

Techstra Solutions is seeking a Senior AI Engineer to design, build, and operationalize enterprise AI applications that combine Anthropic Claude and other large language models with governed enterprise data. This hands-on senior technical role spans AI/LLM engineering, software development, data engineering, and solution architecture, taking projects from architecture and experimentation through secure, reliable, scalable production deployment. The engineer will build applications such as AI agents, copilots, conversational applications, retrieval-augmented generation (RAG) solutions, document intelligence, and workflow automation. The role also covers Databricks-based data pipelines and integration, enterprise governance, production engineering, and collaboration with business, data, architecture, security, governance, and engineering teams. The successful candidate can turn ambiguous business problems into pragmatic AI architectures and personally build critical components.

Requirements

At least 8 years of software, data engineering, analytics engineering, or related technology experience, including significant recent experience building AI/ML or GenAI solutions.
Demonstrated hands-on experience developing applications using Anthropic Claude.
Strong experience integrating commercial LLMs through APIs into production applications.
Experience developing RAG, agentic AI, tool-calling, or other LLM-based enterprise applications.
Strong programming experience with Python.
Significant data engineering experience with Databricks, Apache Spark or PySpark, SQL, Delta Lake, ETL/ELT, data pipelines, and integration.
Experience designing solutions that combine LLMs with enterprise data.
Understanding of embeddings, vector search, semantic retrieval, context management, and document processing.
Experience with API development and application/system integration.
Strong understanding of data governance, data quality, metadata, lineage, security, and access controls.
Experience with cloud platforms such as Azure, AWS, or Google Cloud.
Experience implementing CI/CD and modern software engineering practices.
Ability to independently take a complex AI use case from concept and architecture through working production implementation.

Nice To Haves

Experience with Claude Code in software engineering and development workflows.
Experience with Model Context Protocol (MCP) and tool-enabled or agentic architectures.
Experience with Databricks Unity Catalog and governed enterprise data environments.
Experience building AI applications against large enterprise data estates.
Experience with vector databases or vector search, embeddings, document processing, and semantic search.
Experience modernizing traditional analytics or data platforms to support AI use cases.
Familiarity with MLflow or similar model/application lifecycle and evaluation capabilities.
Experience with Azure DevOps, GitHub, or comparable CI/CD platforms.
Experience implementing AI governance, security, evaluation, and monitoring standards.

Responsibilities

Design and develop enterprise AI applications using Anthropic Claude and Claude APIs.
Build LLM-powered agents, copilots, conversational applications, RAG solutions, document intelligence, and workflow automation.
Develop prompting, structured-output, tool-use, and agentic patterns using Claude.
Integrate Claude-based applications securely with enterprise data, APIs, applications, and business processes.
Implement RAG architectures using structured and unstructured enterprise data.
Develop evaluation frameworks to measure AI solution quality, accuracy, reliability, and business effectiveness.
Implement AI guardrails, observability, exception handling, security, and responsible AI practices.
Optimize AI solutions for performance, reliability, scalability, and cost; assess emerging AI patterns for practical business value.
Design and develop data pipelines for AI, analytics, and machine learning workloads using Databricks, Spark, PySpark, Python, SQL, and Delta Lake.
Prepare enterprise data for AI applications, including ingestion, transformation, enrichment, chunking, metadata, and retrieval processes.
Use Unity Catalog and enterprise governance frameworks to ensure AI solutions rely on governed, secure, and traceable data.
Integrate AI applications with lakehouse, data warehouse, API, document, and operational data sources.
Apply data quality, lineage, metadata, access control, and governance standards to AI solutions.
Define technical architectures from data ingestion through model interaction and application delivery, making decisions across LLMs, data platforms, retrieval technologies, APIs, orchestration frameworks, and cloud services.
Design reusable AI services, frameworks, APIs, and components for multiple business use cases.
Establish development, testing, evaluation, deployment, and monitoring patterns for production AI applications.
Participate in architecture and code reviews and mentor engineers in AI and data engineering practices.
Work with business, data, architecture, security, governance, and engineering teams to translate business problems into implementable AI solutions.
Independently take complex AI use cases from concept and architecture through production implementation.