AI/ML Engineer

Job Title: AI/ML Engineer
Location: Reston, VA (Onsite – 5 Days/Week)
Employment Type: Full-Time

Job Summary

We are seeking a highly skilled AI/ML Engineer. The ideal candidate will have strong experience in designing, developing, and deploying machine learning and generative AI solutions using modern cloud technologies. The candidate should be passionate about building scalable AI applications and integrating ML models into enterprise systems.

Required Skills

• 5+ years of experience in Artificial Intelligence and Machine Learning.
• Strong programming experience in Python.
• Hands-on experience with Machine Learning, Deep Learning, and Natural Language Processing (NLP).
• Experience with Generative AI technologies such as LLMs, LangChain, RAG (Retrieval-Augmented Generation), and AI Agents.
• Experience with TensorFlow, PyTorch, or Scikit-learn.
• Strong knowledge of vector databases such as Pinecone, FAISS, or ChromaDB.
• Experience with cloud platforms (AWS preferred, Azure or Google Cloud Platform acceptable).
• Familiarity with Docker, Kubernetes, and CI/CD pipelines.
• Experience working with REST APIs and microservices architecture.
• Strong SQL skills and experience with structured/unstructured data.
• Excellent analytical and problem-solving abilities.

Preferred Skills

• Experience with AWS Bedrock, Amazon SageMaker, or Azure OpenAI.
• Knowledge of MLOps tools such as MLflow, Kubeflow, or SageMaker Pipelines.
• Experience with prompt engineering and LLM evaluation frameworks.
• Financial services or mortgage industry experience is a plus.
• Experience in Agile/Scrum development environments.

Responsibilities

• Design, develop, and deploy AI/ML models for enterprise applications.
• Build and optimize Generative AI solutions using LLMs and RAG architecture.
• Develop scalable machine learning pipelines for training, inference, and monitoring.
• Collaborate with data engineers and software developers to integrate AI solutions into production systems.
• Fine-tune foundation models and optimize prompts for business use cases.
• Monitor model performance and improve accuracy, scalability, and reliability.
• Participate in architecture discussions and recommend AI best practices.
• Create technical documentation and support production deployments.