Machine Learning Engineer with Security Clearance

Signature Federal Systems is searching for a Software Developer with expertise in artificial intelligence to join its dynamic team. This position centers on developing and implementing AI solutions to strengthen enterprise-level IT operations. The Machine Learning Engineer will collaborate closely with cross-functional teams to design, develop, and deploy AI-driven applications that enhance efficiency, automate processes, and deliver valuable insights.

Responsibilities
· Develop and maintain machine learning pipelines and applications using Python and contemporary machine learning frameworks.
· Implement and optimize algorithms for integrating and deploying large language models (LLMs).
· Build RESTful APIs and microservices to serve machine learning models in production environments.
· Write clean, maintainable, and well-documented code, adhering to object-oriented programming principles.
· Collaborate with cross-functional teams to understand requirements and convert them into technical solutions.
· Manage training data, model artifacts, and application state using SQL, NoSQL, and vector databases.
· Containerize machine learning applications with Docker to ensure consistent deployment across environments.
· Use Git for version control and participate in code reviews to maintain code quality.
· Conduct testing and debugging of machine learning applications to ensure reliability and accuracy.
· Support the deployment and monitoring of AI and machine learning models in cloud environments.
· Stay up to date with emerging trends in machine learning, LLMs, and AI engineering best practices.

Qualifications Required
· Bachelor's degree in computer science, software engineering, data science, or a related technical field, plus five years of professional experience in software development or machine learning engineering.
· Strong proficiency in Python programming, with a thorough understanding of object-oriented programming concepts, design patterns, data structures, and algorithms.
· Experience with development tools and practices, including Git version control, Docker containerization, and database management (SQL and/or NoSQL).
· Knowledge of large language model technologies, including familiarity with orchestration frameworks such as LangChain and LangGraph.
· Understanding of retrieval-augmented generation (RAG) architectures and vector databases (including ChromaDB, Pinecone, Weaviate, or similar) for building intelligent retrieval systems.
· Strong problem-solving skills, attention to detail, excellent communication abilities, and eagerness to learn within a collaborative team environment.

Desired
· Master's degree in computer science or a related field.
· Experience with cloud platforms such as AWS, Azure, or Google Cloud, and knowledge of MLOps practices for machine learning model deployment and monitoring.
· Experience with container orchestration and DevOps, including Kubernetes, Rancher, CI/CD pipelines, and infrastructure automation tools like Ansible.
· Familiarity with enterprise platforms such as ServiceNow, SAP, Tableau, or Splunk.
· Contributions to open-source machine learning projects and familiarity with Agile development methodologies.