Gen AI / Machine Learning Engineer

Gen AI / Machine Learning Engineer (NLP Focus) 📍 Location: Washington, DC (Onsite) 🛂 Work Authorization: Must be authorized to work in the U.S. 🔐 Clearance: Ability to obtain Public Trust or higher (if applicable) Role Overview We are seeking a highly skilled Generative AI / Machine Learning Engineer with strong expertise in Natural Language Processing (NLP) to design, develop, and deploy AI-driven solutions. This role will focus on building scalable ML systems, fine-tuning large language models (LLMs), and implementing NLP pipelines that power enterprise applications. The ideal candidate combines strong theoretical ML knowledge with hands-on engineering experience in modern AI frameworks and cloud-based ML infrastructure. Key Responsibilities • Design, develop, and deploy NLP and Generative AI solutions in production environments • Fine-tune and optimize Large Language Models (LLMs) for domain-specific use cases • Build and maintain ML pipelines for data ingestion, preprocessing, training, and inference • Develop prompt engineering strategies and evaluate model performance • Implement Retrieval-Augmented Generation (RAG) architectures • Work with structured and unstructured text datasets • Conduct model evaluation, error analysis, and performance tuning • Collaborate with data engineers and software teams to integrate AI models into applications • Ensure responsible AI practices including bias mitigation, explainability, and governance • Maintain documentation and contribute to AI best practices and architecture standards Required Qualifications • Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or related field • 5+ years of experience in Machine Learning or AI engineering • 3+ years of hands-on experience with NLP • Strong programming skills in Python • Experience with ML frameworks such as: • PyTorch • TensorFlow • Scikit-learn • Experience working with: • Hugging Face Transformers • OpenAI / LLM APIs • LangChain or similar orchestration frameworks • Experience building and deploying models in cloud environments (AWS, Azure, or GCP) • Knowledge of vector databases (e.g., Pinecone, FAISS, Weaviate) • Strong understanding of: • Embeddings • Tokenization • Text classification • Named Entity Recognition (NER) • Sentiment analysis • Semantic search • Experience with REST APIs and microservices architecture • Familiarity with CI/CD pipelines for ML deployment Preferred Qualifications • Experience with: • RAG architectures • LLM fine-tuning (LoRA, PEFT, etc.) • Distributed training • MLOps tools (MLflow, Kubeflow, SageMaker) • Experience working in regulated or government environments • Exposure to AI governance and compliance frameworks • Experience handling sensitive or classified datasets Nice to Have • Knowledge of reinforcement learning from human feedback (RLHF) • Experience building chatbots, copilots, or AI assistants • Experience with knowledge graphs • Familiarity with Kubernetes and containerization