AI Data Engineer Visa Sponsorship Jobs in Maryland
Maryland's AI data engineer job market centers on the Baltimore-Washington corridor, where federal contractors like Booz Allen Hamilton, Leidos, and SAIC drive consistent demand alongside biotech firms in the BioHealth Capital Region. Rockville, Bethesda, and Columbia are key hiring hubs, and many employers in this corridor have established visa sponsorship programs for qualified candidates.
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Get Access To All JobsJob Description: The AI / Agentic AI Engineer works across: (1) large-scale data pipeline development processing market events in a cloud environment, and (2) primarily, design and development of agentic AI systems including LLM-powered regulatory data assistants, MCP servers, and agent harness architectures. This position contributes to overall product quality throughout the software development lifecycle.
- Build and maintain ETL/ELT pipelines using Apache Spark, Hive, and Trino across S3-based data lake environments
- Develop and optimize SQL for large-scale surveillance datasets including window functions, multi-table joins, and complex aggregations
- Build and engineer big data systems (EMR-on-EC2, EMR-on-EKS) and develop solutions on analytical platforms (SageMaker, Domino, Dataiku)
- Participate in data quality monitoring, anomaly detection, and production incident investigation
- Develop AI agent systems using AWS Bedrock and agent frameworks (Strands Agents SDK, LangChain/LangGraph, or equivalent)
- Build agent harness architectures combining LLM reasoning with deterministic execution - skill/RAG-based SQL generation and structured output validation
- Implement agent memory, context management, and tool integration (MCP servers, API connectors, data catalog lookups) across the data lake
- Build evaluation frameworks for agent accuracy - paraphrase robustness, routing precision, and structural consistency
- Stay informed of advances in LLM frameworks (LangGraph, Google ADK, AWS Strands) and emerging AI capabilities
- Write clean, well-tested code; contribute to CI/CD Jenkins pipelines and infrastructure-as-code on AWS
- Ensure secure handling of RCI and sensitive regulatory data across both data pipelines and agent outputs - auditable execution traces
- Adhere to CLIENT and team standards for secure development practices and technology policies
- Partner across teams, communicate technical information at the appropriate level, and maintain documentation on Confluence/Wiki
- Actively learn from senior team members; contribute to process improvement in line with CLIENT's values of collaboration, expertise, innovation, and responsibility
Essential Technical Skills
Data Engineering & Big Data Technologies
- Experience building data pipelines using Apache Spark (PySpark preferred) and SQL
- Experience with SQL query engines (Hive, Trino/Presto, or similar) and cloud data platforms (AWS S3, EMR, Lambda)
- Understanding of common issues like data skew and strategies to mitigate it, working with large data volumes, and troubleshooting job failures due to resource limitations, bad data, and scalability challenges
- Real-world experience with debugging and mitigation strategies
Generative AI & Agentic Systems
- Practical experience building LLM-powered agent systems that use tools and produce structured outputs (not just chatbot interfaces)
- Hands-on experience with at least one agent framework: LangChain, LangGraph, AWS Strands, or equivalent
- Working knowledge of prompt engineering, RAG architectures, and context/memory management
- Experience with foundation model APIs (Anthropic Claude, Amazon Nova, OpenAI, or similar)
- Memory Architecture: Understanding of agent memory tiers - working memory, episodic memory, semantic memory - and strategies for context persistence, pruning, and retrieval across sessions
- Agent Harness Design: Familiarity with harness patterns that wrap LLM reasoning with deterministic guardrails, tool routing, verification loops, and graceful degradation
AI Tool Proficiency
- Hands-on experience with AI development tools (GitHub Copilot, Q Developer, ChatGPT, Claude, etc.)
- Experience with spec-driven development - using structured specifications to guide AI code generation, review, and validation
- Ability to leverage AI pair programming for code suggestions, debugging, refactoring, and automated test generation
Cloud Technologies
- Experience with AWS services like S3, EMR, EMR on EKS, Lambda, Bedrock, Step Functions, etc
- Hands-on experience using S3 with Spark (e.g., dealing with file formats, consistency issues)
- Familiarity with AWS Bedrock for foundation model invocation, knowledge bases, guardrails, and agent orchestration
- Exposure to Google Cloud Vertex AI (model garden, grounding, agent builder) or equivalent managed AI platforms
- Familiarity with AWS monitoring and logging tools (CloudWatch, CloudTrail) for production workloads
Programming – Python
- Proficiency in Python for data engineering and automation
- Ability to write clean, modular, and performant code
- Experience with functional programming concepts (e.g., immutability, higher-order functions)
- Strong understanding of collections, concurrency, and memory management
SQL Skills (Window Functions, Joins, Complex Queries)
- Proficiency with SQL window functions, multi-table joins, and aggregations
- Ability to write and optimize complex SQL queries
- Experience handling edge cases like NULLs, duplicates, and ordering
Good to Have
- AWS Bedrock AgentCore (memory, identity, tool gateway)
- Model Context Protocol (MCP) server development and integration
- Agent evaluation harnesses and agentic patterns (draft-verification, compile-style generation)
- Fine-tuning foundation models for domain-specific tasks (LoRA, PEFT, or managed fine-tuning via Bedrock/Vertex AI)
- Local model execution with Ollama, vLLM, or similar for development and experimentation
- Vector databases (FAISS, Pinecone, OpenSearch)
- Docker, Kubernetes, and Amazon EKS for containerized workloads
- Infrastructure as Code (Terraform, CloudFormation)
- Experience with CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions, ArgoCD)
- Experience with monitoring and observability tools (Prometheus, Grafana, ELK stack)
- AWS certifications (AI Practitioner, Solutions Architect, or Kubernetes certifications like CKA/CKAD)
Education / Experience Requirement
- Bachelor's degree in Computer Science, Data Science, Information Systems, or related discipline with at least two (2) years of related experience; or equivalent training and/or work experience; past Financial Services industry experience preferred
- Demonstrated technical expertise in Object Oriented and database technologies/concepts which resulted in deployment of enterprise quality solutions
- Extensive knowledge of industry leading software engineering approaches including Test Automation, Build Automation and Configuration Management frameworks
LI-CGTS # TS-2505
Job Description: The AI / Agentic AI Engineer works across: (1) large-scale data pipeline development processing market events in a cloud environment, and (2) primarily, design and development of agentic AI systems including LLM-powered regulatory data assistants, MCP servers, and agent harness architectures. This position contributes to overall product quality throughout the software development lifecycle.
- Build and maintain ETL/ELT pipelines using Apache Spark, Hive, and Trino across S3-based data lake environments
- Develop and optimize SQL for large-scale surveillance datasets including window functions, multi-table joins, and complex aggregations
- Build and engineer big data systems (EMR-on-EC2, EMR-on-EKS) and develop solutions on analytical platforms (SageMaker, Domino, Dataiku)
- Participate in data quality monitoring, anomaly detection, and production incident investigation
- Develop AI agent systems using AWS Bedrock and agent frameworks (Strands Agents SDK, LangChain/LangGraph, or equivalent)
- Build agent harness architectures combining LLM reasoning with deterministic execution - skill/RAG-based SQL generation and structured output validation
- Implement agent memory, context management, and tool integration (MCP servers, API connectors, data catalog lookups) across the data lake
- Build evaluation frameworks for agent accuracy - paraphrase robustness, routing precision, and structural consistency
- Stay informed of advances in LLM frameworks (LangGraph, Google ADK, AWS Strands) and emerging AI capabilities
- Write clean, well-tested code; contribute to CI/CD Jenkins pipelines and infrastructure-as-code on AWS
- Ensure secure handling of RCI and sensitive regulatory data across both data pipelines and agent outputs - auditable execution traces
- Adhere to CLIENT and team standards for secure development practices and technology policies
- Partner across teams, communicate technical information at the appropriate level, and maintain documentation on Confluence/Wiki
- Actively learn from senior team members; contribute to process improvement in line with CLIENT's values of collaboration, expertise, innovation, and responsibility
Essential Technical Skills
Data Engineering & Big Data Technologies
- Experience building data pipelines using Apache Spark (PySpark preferred) and SQL
- Experience with SQL query engines (Hive, Trino/Presto, or similar) and cloud data platforms (AWS S3, EMR, Lambda)
- Understanding of common issues like data skew and strategies to mitigate it, working with large data volumes, and troubleshooting job failures due to resource limitations, bad data, and scalability challenges
- Real-world experience with debugging and mitigation strategies
Generative AI & Agentic Systems
- Practical experience building LLM-powered agent systems that use tools and produce structured outputs (not just chatbot interfaces)
- Hands-on experience with at least one agent framework: LangChain, LangGraph, AWS Strands, or equivalent
- Working knowledge of prompt engineering, RAG architectures, and context/memory management
- Experience with foundation model APIs (Anthropic Claude, Amazon Nova, OpenAI, or similar)
- Memory Architecture: Understanding of agent memory tiers - working memory, episodic memory, semantic memory - and strategies for context persistence, pruning, and retrieval across sessions
- Agent Harness Design: Familiarity with harness patterns that wrap LLM reasoning with deterministic guardrails, tool routing, verification loops, and graceful degradation
AI Tool Proficiency
- Hands-on experience with AI development tools (GitHub Copilot, Q Developer, ChatGPT, Claude, etc.)
- Experience with spec-driven development - using structured specifications to guide AI code generation, review, and validation
- Ability to leverage AI pair programming for code suggestions, debugging, refactoring, and automated test generation
Cloud Technologies
- Experience with AWS services like S3, EMR, EMR on EKS, Lambda, Bedrock, Step Functions, etc
- Hands-on experience using S3 with Spark (e.g., dealing with file formats, consistency issues)
- Familiarity with AWS Bedrock for foundation model invocation, knowledge bases, guardrails, and agent orchestration
- Exposure to Google Cloud Vertex AI (model garden, grounding, agent builder) or equivalent managed AI platforms
- Familiarity with AWS monitoring and logging tools (CloudWatch, CloudTrail) for production workloads
Programming – Python
- Proficiency in Python for data engineering and automation
- Ability to write clean, modular, and performant code
- Experience with functional programming concepts (e.g., immutability, higher-order functions)
- Strong understanding of collections, concurrency, and memory management
SQL Skills (Window Functions, Joins, Complex Queries)
- Proficiency with SQL window functions, multi-table joins, and aggregations
- Ability to write and optimize complex SQL queries
- Experience handling edge cases like NULLs, duplicates, and ordering
Good to Have
- AWS Bedrock AgentCore (memory, identity, tool gateway)
- Model Context Protocol (MCP) server development and integration
- Agent evaluation harnesses and agentic patterns (draft-verification, compile-style generation)
- Fine-tuning foundation models for domain-specific tasks (LoRA, PEFT, or managed fine-tuning via Bedrock/Vertex AI)
- Local model execution with Ollama, vLLM, or similar for development and experimentation
- Vector databases (FAISS, Pinecone, OpenSearch)
- Docker, Kubernetes, and Amazon EKS for containerized workloads
- Infrastructure as Code (Terraform, CloudFormation)
- Experience with CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions, ArgoCD)
- Experience with monitoring and observability tools (Prometheus, Grafana, ELK stack)
- AWS certifications (AI Practitioner, Solutions Architect, or Kubernetes certifications like CKA/CKAD)
Education / Experience Requirement
- Bachelor's degree in Computer Science, Data Science, Information Systems, or related discipline with at least two (2) years of related experience; or equivalent training and/or work experience; past Financial Services industry experience preferred
- Demonstrated technical expertise in Object Oriented and database technologies/concepts which resulted in deployment of enterprise quality solutions
- Extensive knowledge of industry leading software engineering approaches including Test Automation, Build Automation and Configuration Management frameworks
LI-CGTS # TS-2505
AI Data Engineer Job Roles in Maryland
See all 172+ AI Data Engineer Jobs in Maryland
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Search AI Data Engineer Jobs in MarylandAI Data Engineer Jobs in Maryland: Frequently Asked Questions
Which companies sponsor visas for AI data engineers in Maryland?
Federal contractors and defense technology firms are among the most active sponsors in Maryland, including Booz Allen Hamilton, Leidos, SAIC, and Northrop Grumman. In the commercial sector, biotech and health IT companies such as Veeva Systems and various NIH-adjacent research organizations have also sponsored AI data engineer roles. Sponsorship availability varies by role, clearance requirements, and current headcount needs.
Which visa types are most common for AI data engineer roles in Maryland?
The H-1B visa is the most common visa category for AI data engineers in Maryland, as the role typically qualifies as a specialty occupation requiring at least a bachelor's degree in computer science, data science, or a related field. Candidates already holding L-1B, O-1 visa, or F-1 OPT status may also find pathways through Maryland employers, particularly at larger contractors with established immigration support infrastructure.
How to find ai data engineer visa sponsorship jobs in Maryland?
Migrate Mate filters job listings specifically for visa sponsorship eligibility, making it straightforward to browse AI data engineer openings in Maryland without sorting through roles that won't support international candidates. You can narrow results to Maryland employers across the Baltimore-Washington corridor, including federal contractors, health IT firms, and research-driven organizations that have a track record of sponsoring technical roles.
Which cities in Maryland have the most AI data engineer sponsorship jobs?
Bethesda, Rockville, and Columbia account for a significant share of Maryland's AI data engineer sponsorship activity, largely due to concentration of federal agencies, contractors, and biotech companies in Montgomery County and Howard County. Baltimore also has a growing presence, driven by academic medical centers like Johns Hopkins and University of Maryland Medical System, which increasingly hire data engineering talent for clinical AI initiatives.
Are there state-specific considerations for AI data engineers seeking sponsorship in Maryland?
Maryland's high concentration of federal contractors introduces a clearance dimension that affects international candidates. Many federal contract roles require U.S. citizenship or permanent residency for security clearance eligibility, which can limit sponsorship opportunities at those employers specifically. Candidates without clearance eligibility often find better pathways through commercial biotech, health IT, or academic research employers operating in the state's BioHealth Capital Region.
What is the prevailing wage for sponsored ai data engineer jobs in Maryland?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.