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