DRS IT Solutions IncRemote
Lead Machine Learning Engineer @ Remote
United States of America
Contract
5 days ago
No H1B
Key skills
Machine LearningDeep LearningML AlgorithmsLinear AlgebraProbabilityStatisticsData PipelinesModel DeploymentMLOpsMLflowAzure MLVertex AICloud-Native AIAWSGCPAzureLarge Language ModelsPrompt EngineeringModel Fine-tuningRetrieval-Augmented GenerationAI EvaluationA/B TestingPyTorchJAXTensorFlowVector DatabasesAI Model MonitoringSoftware EngineeringAIMLLLMRAGPineconeWeaviateLeadershipMentoring
About this role
DRS IT Solutions Inc is seeking a Senior or Lead Machine Learning Engineer to join their Applied AI team. The role involves designing and implementing advanced AI-driven solutions, focusing on machine learning systems for various applications.
Responsibilities:
- Design, fine-tune, and implement state-of-the-art language model applications and machine learning systems
- Integrate AI-powered solutions for fraud detection, decision automation, and process optimization
- Provide technical leadership and hands-on engineering expertise for deploying AI and ML models at scale
Requirements:
- 5+ years of hands-on experience in machine learning engineering, with a strong track record delivering large-scale AI/ML systems from research to production
- Deep expertise in ML algorithms, deep learning architectures, and the underlying mathematical foundations — particularly linear algebra, probability, and statistics
- Proven proficiency in working with large-scale datasets and building efficient, reliable AI data pipelines
- Hands-on experience packaging and deploying ML models as APIs for seamless integration into production environments
- Familiarity with MLOps tooling and platforms such as MLflow, Azure ML, or Vertex AI, with an understanding of model lifecycle management
- Experience with cloud-native AI architectures, including distributed model training and scalable deployment patterns on AWS, GCP, or Azure
- Strong background in LLM architectures, prompt engineering, fine-tuning, model adaptation, and RAG techniques
- Robust understanding of AI evaluation methodologies, testing frameworks, and A/B testing for AI-driven applications
- Proficiency with PyTorch, JAX, or TensorFlow
- Knowledge of vector databases (e.g., Pinecone, Weaviate, pgvector) and AI model monitoring practices including drift detection and governance
- Strong software engineering fundamentals, with demonstrated ability to write clean, maintainable, and production-quality AI code
- Experience mentoring engineering teams and driving AI adoption across cross-functional groups
- Bachelor's, Master's, or PhD in Computer Science, a related field, or equivalent practical experience, with a focus on machine learning
- Publications, patents, or open-source contributions in AI/ML are a plus