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 production-grade enterprise AI applications using Anthropic Claude and governed enterprise data, including data managed on Databricks. This hands-on senior technical role combines AI/LLM engineering, software development, data engineering, and solution architecture. The engineer will take solutions from architecture and experimentation through production deployment, building reliable, secure, and scalable applications that integrate large language models with enterprise data, APIs, applications, and business processes. The role focuses on Claude-powered agents, copilots, conversational applications, RAG solutions, document intelligence, and workflow automation. It also encompasses data pipelines and governed lakehouse solutions using Databricks, Spark, PySpark, Python, SQL, and Delta Lake. The engineer will work with business, data, architecture, security, governance, and engineering teams to turn business problems into practical AI solutions, and will participate in architecture and code reviews and mentor other engineers.

Requirements

At least 8 years of software, data engineering, analytics engineering, or related technology experience, with significant recent experience building AI/ML or GenAI solutions.
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 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 or 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.
Build secure integrations between Claude applications and enterprise data, APIs, applications, and business processes.
Implement RAG architectures using structured and unstructured enterprise data.
Develop evaluation frameworks to measure 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, and assess emerging AI patterns for practical business value.
Design and develop data pipelines for AI, analytics, and machine learning workloads.
Build scalable data solutions using Databricks, Spark, PySpark, Python, SQL, and Delta Lake.
Prepare structured and unstructured enterprise data for LLM and AI applications.
Design ingestion, transformation, enrichment, chunking, metadata, and retrieval processes for RAG and agentic AI solutions.
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 data-governance standards to AI solutions.
Define technical architectures for enterprise AI solutions from data ingestion through model interaction and application delivery.
Make architecture 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.
Collaborate 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.