Sr. AI/ML Engineer

Our client seeks a highly qualified hands-on Sr. AI /ML Engineer for a full time/direct hire opportunity with our client; this position is hybrid (2-3 days) in their Washington, DC office.

We are seeking a highly technical, hands-on AI/ML Engineer with architect-level capabilities who is passionate about building, deploying, and optimizing AI systems at scale. This is an individual contributor role focused on deep technical execution rather than people management. The ideal candidate will have extensive experience developing production-grade AI/ML solutions, deploying large-scale inference systems, and leveraging Kubernetes to operate highly scalable AI platforms.

Experience/Skills background:

• Hands-on development and implementation of AI/ML solutions (non-negotiable)
• Fine-tuning and optimization of LLMs and smaller language models
• Inference engineering, model serving, and endpoint deployment
• Reinforcement learning and human-in-the-loop feedback systems
• Agentic AI architectures and multi-agent environments
• End-to-end AI application development and deployment
• AI infrastructure cost optimization and governance
• Kubernetes-based deployment, orchestration, and scaling of AI workloads
• Large Language Models (LLMs) and Small Language Models (SLMs)
• LoRA, PEFT, quantization, and model optimization techniques
• Building proprietary AI models using internal datasets
• Agent frameworks, orchestration platforms, and autonomous workflows
• MLOps and AIOps platforms such as Kubeflow
• Kubernetes for container orchestration, model serving, autoscaling, GPU workload management, and production operations
• Distributed AI/ML systems and cloud-native architectures

Responsibilities:

• Design, build, and deploy AI/ML models into production environments.
• Fine-tune, customize, and optimize LLMs and smaller models for enterprise use cases.
• Develop scalable inference pipelines and high-performance model-serving endpoints.
• Architect and implement agentic AI solutions and multi-agent workflows.
• Build reinforcement learning systems incorporating feedback loops and human-in-the-loop methodologies.
• Develop proprietary AI solutions leveraging internal datasets and domain-specific knowledge.
• Optimize model performance, inference latency, GPU utilization, token consumption, and infrastructure costs.
• Design, deploy, and manage AI platforms on Kubernetes, including model serving, workload orchestration, autoscaling, and production operations.
• Build and maintain MLOps/AIOps pipelines using tools such as Kubeflow, MLflow, and Kubernetes-native workflows.
• Collaborate with data engineers, software engineers, and platform teams to integrate AI capabilities into enterprise applications.
• Drive best practices for AI system reliability, observability, scalability, and security.