Applied AI Engineer

Our Client, a Real Estate and Financial Services company, is looking for an Applied AI Engineer for their McLean, VA location.
 
Responsibilities:

• Architect and implement scalable AI Agents, Agentic Workflows, and GenAI applications for complex business use cases.
• Develop and evaluate LLMs (e.g., Claude, Azure OpenAI, open-source models).
• Seek candidates with experience fine-tuning open weight models.
• Design and deploy systems using vector databases and knowledge bases.
• Curate enterprise data with connectors integrated into AWS Bedrock Knowledge Base/Elastic.
• Implement Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication.
• Build and maintain Jupyter notebooks on platforms like SageMaker, MLFlow, and Kubeflow (on Kubernetes/EKS).
• Integrate GenAI solutions with enterprise platforms via APIs and standardized GenAI patterns.
• Establish validation procedures using Evaluation Frameworks (DeepEval), bias mitigation, safety protocols, and guardrails.
• Design robust ingestion pipelines for extracting, chunking, enriching, and anonymizing data from various media sources.
• Orchestrate multimodal pipelines with scalable frameworks (e.g., Apache Spark, PySpark) for automated ETL/ELT workflows.
• Implement embedding-driven workflows, mapping media content to vector representations and integrating with vector stores (AWS KnowledgeBase/Elastic/Mongo Atlas).

• Bachelor’s degree in Computer Science, Data Science, AI/ML, or related technical field (Master’s preferred).
• Advanced proficiency in Python, big data technologies (e.g., Spark, Hadoop), and data manipulation libraries (e.g., Pandas, NumPy).
• Solid knowledge of classical ML algorithms, deep learning architectures, LLMs, generative AI, and reinforcement learning.
• Hands-on experience with Microsoft Azure, Azure OpenAI, and AWS cloud platforms.
• Strong understanding of Spec-Based Development, Git, and CI/CD best practices.
• Expertise in prompt engineering, vector databases (AWS KnowledgeBase/Elastic), and multimodal models.
• Proven experience with cloud-native AI development (AWS SageMaker, Bedrock, MLFlow on EKS).
• Strong programming skills in Python and ML libraries (Transformers, LangChain, etc.).
• Deep understanding of GenAI architectural patterns and Evaluation Frameworks (DeepEval).
• Demonstrated ability to work in cross-functional agile teams.
• Github Code Repository Link required for each candidate.
• Thorough vetting is mandatory.
• Preferred Qualifications:
• Experience in financial, regulatory technology, or enterprise automation.
• Familiarity with MLOps tools (MLflow, Kubeflow, Weights & Biases).
• Contributions to open-source AI projects or research publications.
• Experience with containerization and orchestration (Docker, Kubernetes).
• Experience with Spark is optional but preferred.

• Health Benefits
• Referral Program
• Excellent growth and advancement opportunities

• Health Benefits
• Referral Program