AI/ML Software Engineer

Position: AI/ML Software Engineer

The client seeks an AI/ML Software Engineer to build software tools that incorporate AI/ML techniques to automate narrowly defined tasks with high accuracy, assist internal users with their job functions, and improve the external user experience. This includes, but is not limited to, RPA work, building or refining chatbots, incorporating AI/ML into reporting tools, building LLM agents for knowledge retrieval, deep research, translation, transcription, redaction, document analysis, document generation, agentic coding, and data processing.

Contract duration: Five years

Interview mode: Virtual via MS Teams or Zoom

Location: 189 Harry S Truman Parkway, Annapolis, MD 21401 (100% remote but the resource must be onsite for the first two days AND must be able to report onsite within 72 hours after notification)

No Visa restrictions

Duties/Responsibilities

System Design & Collaboration:

Work within established constraints regarding infrastructure, programming languages, and model selection

Contribute to technical decision-making related to data processing, retrieval strategies, and system integration

Collaborate with team members to define agent architectures, workflows, and system design decisions

Evaluate and select appropriate approaches for given tasks, including determining when to use LLM-based versus non-LLM techniques

Designing and building software systems that integrate AI/ML techniques to automate tasks, assist internal users, and improve user-facing services.

Testing, Evaluation, and Quality Assurance:

Assist in the design and implementation of testing and evaluation pipelines for AI/ML systems

Develop unit and integration tests for AI-enabled workflows and data pipelines

Generate and utilize synthetic data to support evaluation and benchmarking efforts

Contribute to improving system performance, including accuracy, latency, and cost efficiency

Deployment & Operations:

Support deployment of AI/ML applications within a hybrid cloud environment

Work with containerized applications to ensure reliable deployment and updates

Optimize systems for environments with limited computational resources, including minimal GPU availability

General Responsibilities:

Deliver production-grade systems aligned with defined requirements, while supporting iterative improvement of evolving tools

Document system designs, workflows, and technical decisions as required

Stay informed on relevant advancements in AI/ML and apply them where appropriate within project constraints

Minimum Qualifications

• Three (3) years' experience in data science, machine learning, or applied AI development.

• Three (3) years' experience in software engineering, architecture, or web development.

• Experience with:

• SQL and relational database systems (e.g., PostgreSQL)

• Fine-tuning small language models or embedding models

• Contributing to or maintaining open-source software projects

• Graph databases or graph extensions (e.g., Neo4j, Apache AGE)

• Designing and implementing multi-agent or task-oriented AI systems

• Embedding models, vector similarity, re-ranking, and graph retrieval techniques in RAG systems

• Version control systems (e.g., Git), containerization technologies (e.g., Docker), and service-oriented architecture

• Collaborating with large language models (LLMs), including both API-based integration and local deployment

• Validating AI-generated outputs, mitigating hallucinations, and integrating AI tools into production service pipelines

• Ability to:

• Understand data structures, algorithms, and clean coding principles

• Select and apply appropriate techniques (LLM and non-LLM) based on task requirements

• Develop and improve testing and evaluation pipelines for AI systems, including use of synthetic data

• Demonstrate proficiency in Python, including the ability to develop production-grade backend services, APIs, middleware, and data pipelines.

• Design and implement AI/ML systems that operate effectively on complex, inconsistent, or evolving datasets while balancing accuracy, latency, and cost (token consumption)

• Collaborate with team members to define system architecture, agent workflows, and data pipelines while working in constrained environments, including limited GPU availability and predefined infrastructure

• Knowledge of:

• Hybrid cloud environments and distributed system considerations

• Threading, asynchronous processing, and queues in backend servers

• React and Microsoft Teams Toolkit for developing chatbot user interfaces

• Non-LLM data analysis techniques for structured, semi-structured, and unstructured data

• Classical natural language processing (NLP) techniques in addition to LLM-based approaches

• Data science and LLM-related libraries in Rust or other performance-oriented programming languages

• Education: Bachelor of Science in Engineering, Computer Science, Data Science, or Mathematics, or a related field.