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AI/ML Software Engineer

Annapolis, Maryland

ASSYST is seeking an AI/ML Software Engineer to build tools for automation, internal user assistance, and external user experience, including chatbots, RPA, and knowledge retrieval for our client in Annapolis, Maryland (Hybrid Role).

This is a Hybrid role that requires the selected candidate to work onsite at Annapolis, Maryland for the first two days. 

Responsibilities:

  • Design and build software systems that integrate AI/ML techniques to automate tasks, assist internal users, and improve user-facing services.
  • Build software tools such as RPA workflows, chatbots, knowledge retrieval agents, and document analysis systems.
  • Contribute to technical decision-making regarding data processing, retrieval strategies, and system integration within established infrastructure constraints.
  • Collaborate with team members to define agent architectures, workflows, and system design decisions.
  • Evaluate and select appropriate approaches for tasks, including determining when to use LLM-based versus non-LLM techniques.
  • Design and implement testing and evaluation pipelines for AI/ML systems using unit tests and integration tests.
  • Generate and utilize synthetic data to support system evaluation and benchmarking efforts.
  • Optimize system performance in terms of accuracy, latency, and cost efficiency.
  • Support the deployment of containerized AI/ML applications within a hybrid cloud environment.
  • Optimize systems for environments with limited computational resources and minimal GPU availability.
  • Deliver production-grade systems aligned with defined requirements while supporting iterative tool improvement.
  • Document all system designs, workflows, and technical decisions.
  • Stay informed on relevant advancements in AI/ML and apply them where appropriate within project constraints.
  • Complete specific year-by-year deliverables including internal chatbot refinement, translation agents, and redaction tools.
  • Provide high-level summaries of hours expended by deliverable with each invoice to ensure transparency.

Requirements:

  • Bachelor of Science in Engineering, Computer Science, or a related field, with preferred experience of three (3) years in data science/AI and three (3) years in software engineering.
  • Minimum of three years of experience in data science, machine learning, or applied AI development.
  • Minimum of three years of experience in software engineering, architecture, or web development.
  • Demonstrated proficiency in Python for developing production-grade backend services and data pipelines.
  • Experience with SQL and relational database systems such as PostgreSQL.
  • Experience with embedding models, vector similarity, and graph retrieval techniques in RAG systems.
  • Experience with version control systems, containerization technologies like Docker, and service-oriented architectures.
  • Experience collaborating with Large Language Models (LLMs) via both API integration and local deployment.
  • Experience validating AI-generated outputs and mitigating hallucinations in production pipelines.
  • Experience fine-tuning small language models or embedding models.
  • Experience with graph databases or extensions such as Neo4j or Apache AGE.
  • Knowledge of React and Microsoft Teams Toolkit for developing chatbot user interfaces.
  • Knowledge of threading, asynchronous processing, and queues in backend servers.
  • Knowledge of hybrid cloud environments and distributed system considerations.
  • Knowledge of classical natural language processing (NLP) techniques and non-LLM data analysis.
  • Knowledge of data science libraries in Rust or other performance-oriented programming languages.
  • Ability to understand data structures, algorithms, and clean coding principles.

 

ASSYST is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, disability, military status, national origin or any other characteristic protected under federal, state, or applicable local law

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