AI Developer

• Design and develop AI models, algorithms, and applications to solve specific business or mission problems, across multi-cloud, multi-platform, and model-agnostic environments.

• Collaborate with team members to preprocess, clean, and analyze large datasets in support of model development and evaluation.

• Implement machine learning and deep learning algorithms and frameworks to build robust, production-ready AI solutions.

• Fine-tune AI models to improve performance, accuracy, and efficiency for specific use cases.

• Integrate AI models into production systems, ensuring reliability, scalability, and maintainability.

• Document code, algorithms, and processes thoroughly for future reference - documentation must include all information necessary for another engineer to assume ongoing maintenance of the model or system (e.g., architecture decisions, data sources/lineage, dependencies, configuration, known issues, and retraining/monitoring procedures).

• Stay current with the latest AI technologies, models, platforms, and frameworks, and recommend adoption where it benefits the organization.

• Ensure solutions meet security, compliance, and data governance requirements appropriate to a Public Trust environment.

• Bachelor’s degree in Computer Science, Data Science, Engineering, or related field (or equivalent practical experience).

• 4+ years of experience developing AI/ML solutions, with demonstrated experience in data science (statistical modeling, predictive analytics, or similar).

• Hands-on experience across at least two major cloud platforms (AWS, Azure, GCP).

• Experience working with multiple AI/ML model types or providers in a way that is not tied to a single vendor (e.g., building abstraction layers, using orchestration frameworks like LangChain/LlamaIndex, or working across proprietary and open-source models).

• Strong programming skills in Python (or similar), with experience in ML frameworks such as TensorFlow, PyTorch, or scikit-learn.

• Experience with data engineering fundamentals: SQL, data pipelines, data warehousing/lakes.

• Must be eligible for a Public Trust clearance (U.S. citizenship or otherwise eligible status generally required; ability to pass a background investigation).

• Strong communication skills and ability to work effectively in a hybrid team environment.

Preferred Qualifications

• Master’s degree in a relevant technical field.

• Experience with containerization and orchestration (Docker, Kubernetes) for portable AI deployments.

• Familiarity with MLOps practices and tools (MLflow, Kubeflow, CI/CD for ML).

• Prior experience supporting government, public sector, or regulated industry clients.

• Relevant certifications (e.g., AWS/Azure/GCP AI or ML certifications, Certified Data Management Professional).

• Experience with retrieval-augmented generation (RAG), agentic AI systems, or LLM fine-tuning/evaluation.

4.54.5 out of 5 stars Washington, DC Hybrid work $145,000 - $165,000 a year - Full-time

Platinum Technologies is a Northern Virginia based integrated solutions firm that specializes in Cybersecurity, Cloud and Digital Services to the Public Sector. Our team solves hard problems and helps our Mission Partners achieve their goals. If you are self-motivated, possess demonstrated learning agility, and are passionate about delivering high-quality work products - we want to hear from you.

We lead with technical expertise, but that is just the tip of the iceberg - the ‘Why’ matters. At Platinum, we don’t hire people to do a job. We provide professional and leadership development to complement our self-motivated domain experts. Our teammates are dot-connecting leaders that operate in a mutually accountable environment to deliver thought leadership, expert technical analysis, and quality execution for our clients.

You

Platinum Technologies is seeking an experienced AI Developer with Data Science expertise to design, build, and deploy intelligent solutions across diverse technical environments. The ideal candidate is comfortable operating in multi-cloud, multi-platform, and model-agnostic settings - architecting AI/ML solutions that are portable, scalable, and not locked into a single vendor’s ecosystem. This role blends hands-on AI/ML engineering with data science rigor, and requires the judgment to select the right cloud, platform, and model for the problem at hand rather than defaulting to a single toolchain.