Software Development Manager (AI/ML) with Security Clearance

Key Responsibilities

• AI/ML Integration: Drive the seamless integration of state-of-the-art AI/ML models and data science capabilities into a high-availability production environment.
• Architecture & Scalability: Architect, optimize, and enhance microservices-based platforms, ensuring extreme performance, robust security posture, and seamless handling of massive raw data ingest pipelines.
• Hands-on Development & Prototyping: Design, write, and unit-test high-quality code. Build rapid, high-fidelity prototypes to solidify functional requirements and drive technical strategy.
• Technical Leadership & Stakeholder Management: Deconstruct complex technical concepts into discrete development tasks, lead iterative Agile sprints (Scrum/Kanban), and deliver high-impact demonstrations to both technical and non-technical stakeholders.
• Documentation & Governance: Champion the creation of comprehensive technical documentation, application workflow blueprints, and compliance procedures to support program milestones and control gates.

Required Experience & Technical Skills

• Polyglot Backend Expertise: 4–6 years of deep backend proficiency splitting production environments between Python (preferred language) and Java utilizing Object-Oriented Design patterns.
• Modern Frontend Systems: 4–6 years of hands-on experience building sleek, scalable user interfaces using Angular.
• Cloud Architecture & Infrastructure: 6–8 years of expertise designing and deploying secure, enterprise-level architectures within Amazon Web Services (AWS) or equivalent secure government cloud environments.
• Enterprise Search & Analytics: 6–8 years working heavily with massive search engine frameworks such as Elasticsearch, OpenSearch, or Solr/Lucene.
• Databases & Containers: 6–8 years managing a diverse database landscape across both SQL and NoSQL paradigms, paired with deep containerization experience using Docker or Podman.
• Advanced Linux Engineering: 6–8 years of intensive Linux system administration experience, including shell scripting, drive mounting, environment configuration, and memory/process performance diagnostic diagnostics.
• Data Pipelines & AI Foundations: 2–3 years of direct exposure to data science concepts, machine learning development workflows, and 2–4 years of heavy ETL pipeline engineering with massive, messy data sets.
• Agile Workflows: Proven experience leading development sprints using Jira, Confluence, and GitHub/GitLab.

Highly Desired (Bonus) Skills

• Advanced text analytics implementations (e.g., entity extraction, sentiment analysis, document summarization, and categorization).
• Experience evaluating state-of-the-art ML algorithms and building out ML-focused Proof of Concepts.
• Exposure to industry-standard ML and Natural Language Processing (NLP) frameworks like PyTorch, TensorFlow, Keras, spaCy, or NLTK.
• Familiarity with advanced data disciplines like Optical Character Recognition (OCR), Named Entity Recognition (NER), BERT models, image recognition, or fuzzy search indexing.
• Experience utilizing data visualization toolsets (e.g., Pandas, Tableau, D3.js).