Applied AI Engineer

Mclean, VA Contract To Hire Nov 10, 2026

Applied AI Engineer -

Key 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).

Required Qualifications:

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.

Work Conditions:

Collaborative, cross-functional agile environment; on-site.