Responsibilities
- Design and implement machine learning models and AI solutions, including natural language processing (NLP), large language models (LLMs), and deep learning architectures.
- Build end-to-end ML pipelines from data ingestion and preprocessing through model training, evaluation, and production deployment.
- Operationalize models using MLOps practices and tools such as MLflow, Docker, and cloud-native services.
- Deploy models into production environments using SageMaker, Vertex AI, TorchServe, FastAPI, or similar deployment frameworks.
- Collaborate with software engineers, DevOps teams, and product owners to ensure integration of models into applications and workflows.
- Monitor model performance, retraining schedules, and observability metrics to ensure reliability and compliance.
- Translate complex data and model outputs into actionable insights for technical and non-technical stakeholders.
- Participate in Agile ceremonies, code reviews, and model documentation to support collaboration and transparency.
Basic qualifications
- Bachelor’s degree in Computer Science, Engineering, or related field.
- Minimum 3 years of experience in software/ML engineering.
- Proven experience leading and delivering end-to-end AI/ML projects in production environments.
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
- Experience building NLP pipelines, working with transformer-based models, or developing applications using LLMs.
- Hands-on knowledge of MLOps tools and practices including MLflow, model versioning, containerization (Docker), and CI/CD for ML.
- Experience deploying models to production using cloud platforms such as AWS SageMaker, GCP Vertex AI, or custom APIs like FastAPI or TorchServe.
- Familiarity with data engineering principles and integration with upstream data pipelines.
- Ability to collaborate across disciplines, mentor junior team members, and communicate technical solutions clearly.
Preferred qualifications
- Experience with LLM fine-tuning, Retrieval-Augmented Generation (RAG), or generative AI solutions.
- Prior contributions to open-source ML/AI projects or model repositories (e.g., Hugging Face).
- Experience applying AI in regulated domains such as finance, healthcare, or government.
- Familiarity with model interpretability, fairness, and bias mitigation techniques.
- Exposure to secure AI deployment practices in compliance with federal privacy and governance standards (e.g., FISMA, IRS Pub 1075).
- Strong critical thinking and problem-solving skills with a bias toward action and experimentation.
- Excellent communication skills, capable of bridging the gap between business goals and technical execution.
- Collaborative mindset with a willingness to mentor, receive feedback, and iterate quickly.
- Ability to prioritize and manage multiple projects in a fast-paced Agile environment.
- Commitment to producing interpretable, auditable, and ethical AI solutions.
- You can join one of the fastest growing companies headquartered in the Washington DC Metro Area. We give you the opportunity to work in different sectors, so you have the chance at variety while maintaining stability.
- Flexibility at BLN24 allows each individual the opportunity to balance quality work and their personal lives. Depending on projects, we allow remote working opportunities so you can always be in the game no matter where you call home.
Tags & focus areas
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