ML Engineer

🚀 Join a Mission-Driven AI Team That Actually Builds Things

Our client is pushing the boundaries of what's possible with Enterprise AI and Intelligent Exploration—and we want youon the journey. We're on the hunt for a Machine Learning Engineer who’s ready to dive deep into complex challenges and make real impact across industries. If you’re someone who loves blending ML, data engineering, and software development—and are eligible for (or already have) a U.S. government security clearance—this might just be your dream role.

This isn’t one of those "ML in theory only" gigs. You’ll actually build stuff, ship it, and see it used in the wild.

🌟 What You’ll Be Doing:

• 🔧 Lead the charge on platform upgrades to keep our tools cutting-edge, fast, and future-proof.
• 📊 Build sleek, interactive dashboards using the client’s AI Platform Python SDK. Your visuals will help decision-makers see through the noise.
• ⚡ Tune data access patterns for speed and scalability—because milliseconds matter.
• 🛠️ Diagnose and solve runtime performance bottlenecks like a pro.
• 🧱 Design and build scalable, intuitive apps that stand the test of time (and user expectations).
• 🤝 Team up with Technical Product Managers to fine-tune user experience and deliver polished, high-impact solutions.

🙅‍♂️ What You Won’t Be Doing:

• 🧪 Getting stuck in a research rabbit hole with no end product. Here, theory meets execution.
• 🏝️ Working in a silo. You’ll be hands-on across ML, data engineering, and full-stack dev.

📈 Bonus:

This role isn’t a ceiling—it’s a launchpad. If you’ve got your eyes set on senior or tech lead roles, we’ve got the ladder ready.

Here's what we want to see:

• 4+ years of experience Python-based data engineering with a proven track record of deploying ML solutions in production, preferably in security-sensitive environments.
• Proven track record of deploying software into production environments.
• Experience in Machine Learning Engineering roles and the end-to-end lifecycle of AI applications, from model development to deployment.
• 1+ year of experience with technologies like task schedulers (e.g. Celery, Airflow, Prefect, etc.) and web-app development stacks (e.g. Flask/Django) or app building kits like Streamlit/Plotly Dash.
• Background in defense-related applications is helpful but not required
• CS or related technical degree
• Proficiency in Python with a solid understanding of Python Data Stack (pandas, NumPy, scikit-learn, PyTorch, Matplotlib, etc.).
• Experience with big data technologies and frameworks (Spark, Databricks, Snowflake, etc).
• Familiarity with Docker, Kubernetes, and Git.
• Experience with Predictive Maintenance, US AF System of Record Data Sources, Supply Chain, Scheduling Optimization, etc.
• Experience with Cyber Analytics, PCAP and network monitoring, CVEs and Cyber Vulnerabilities, etc.