Software Engineer AI Visa Sponsorship Jobs in Maryland
Maryland's AI engineering market centers on the Baltimore-Washington corridor, where federal contractors like Booz Allen Hamilton, Leidos, and SAIC regularly hire software engineers focused on machine learning and AI systems. The National Security Agency's proximity in Fort Meade and a dense cluster of biotech and cybersecurity firms create consistent demand for AI engineering talent with visa sponsorship.
Find Software Engineer AI JobsOverview
Showing 5 of 163+ Software Engineer AI Jobs in Maryland with Visa Sponsorship
See all 163+ Software Engineer AI Jobs in Maryland with Visa Sponsorship
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Software Engineer AI Jobs in Maryland with Visa Sponsorship.
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
Software Engineer AI Job Roles in Maryland
See all 163+ Software Engineer AI Jobs in Maryland
Sign up for free to filter by visa type, set job alerts, and find employers with verified sponsorship history.
Search Software Engineer AI Jobs in MarylandSoftware Engineer AI Jobs in Maryland: Frequently Asked Questions
Which companies sponsor visas for software engineer AIs in Maryland?
Federal contractors and defense technology firms dominate Maryland's AI sponsorship activity. Booz Allen Hamilton, Leidos, SAIC, and Northrop Grumman have all filed H-1B visa petitions for AI and machine learning engineering roles in the state. Outside the defense sector, companies in the Baltimore biotech corridor and financial technology firms in the DC suburbs also sponsor AI engineers with some regularity.
Which visa types are most common for software engineer AI roles in Maryland?
The H-1B is the most common visa category for AI software engineers in Maryland, used by both defense contractors and private-sector employers. Australians may qualify for the E-3 visa, which skips the lottery. Canadians and Mexicans can pursue TN visa status under the USMCA. Some candidates with exceptional research records explore the O-1A or EB-1A pathways, particularly those coming from university AI programs.
Which cities in Maryland have the most software engineer AI sponsorship jobs?
Bethesda, Rockville, and the greater Montgomery County area see the heaviest concentration of AI engineering sponsorship jobs, driven by proximity to federal agencies and large contractor campuses. Baltimore is the second major hub, particularly for health AI and cybersecurity roles tied to Johns Hopkins and the University of Maryland. The Annapolis Junction area near Fort Meade also generates demand around national security AI work.
How to find software engineer AI visa sponsorship jobs in Maryland?
Migrate Mate filters job listings specifically for roles that offer visa sponsorship, so you can search software engineer AI positions in Maryland without sifting through listings that won't support international candidates. Given how many Maryland postings are tied to federal contracts with clearance requirements, Migrate Mate helps identify which openings are realistically accessible to candidates who need sponsorship and do not yet hold a clearance.
Are there state-specific considerations for AI software engineers seeking sponsorship in Maryland?
Maryland's heavy federal contractor presence creates an important caveat: many AI engineering roles require a U.S. security clearance, which is only available to U.S. citizens and permanent residents. International candidates on H-1B or similar visas are generally ineligible for these positions. Focusing on private-sector employers in biotech, fintech, and commercial AI, as well as university-affiliated research roles, gives sponsored candidates a more realistic set of targets in the state.
What is the prevailing wage for sponsored software engineer ai 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.