AI Engineer Standard II

Title: Developer Premium II Agentic AI engineer
Duration: 6 Months - Long Term
Location: Washington, DC 20433

Hybrid Onsite: 4 days per week from Day 1, with a full transition to 100% onsite anticipated soon.

Background
The Client is accelerating adoption of advanced AI capabilities, including agentic automation, retrieval augmented generation (RAG), model lifecycle improvements, and analytics modernization. To support these efforts, the team requires an AI Developer with strong hands on expertise in modern AI frameworks, cloud-based AI services, and large scale data engineering environments.

Objectives
The objective of this engagement is to design, build, and operationalize AI-powered solutions leveraging enterprise-approved cloud platforms, ensuring they meet functional, data governance, and security requirements.
The developer will:

• Build and enhance AI components that incorporate reasoning, automation, search, and orchestration capabilities.
• Design RAG and agentic AI pipelines aligned with emerging enterprise patterns.
• Support data modeling, analytics, and scalable compute environments for model training, tuning, and evaluation.

• Develop multi-agent pipelines using agentic frameworks (e.g., LangChain, LangGraph, or similar).
• Implement tool driven workflows for retrieval, reasoning, diagnostics, and decision-making.
• Integrate enterprise LLM services and apply responsible AI guidelines.

• Build AI and data pipelines using Azure services such as Azure OpenAI, Cognitive Search, Functions, App Services, and ML Studio.
• Integrate AWS search capabilities (e.g., OpenSearch) where cross-cloud indexing or semantic search is required.
• Develop containerized or serverless compute components (Azure Functions, AWS Lambda, ACR/ECR, etc.).

• Use Databricks for data preparation, feature engineering, large-scale processing, and RAG ready embeddings pipelines.
• Develop statistical models, evaluation metrics, and experiment tracking workflows.
• Implement pipelines for model drift, retrieval accuracy, and quality monitoring.

• Build APIs, automation services, and backend components using Python (FastAPI, Flask, etc.).
• Integrate AI components with enterprise systems, data platforms, or UI layers as needed.
• Develop unit tests, CI/CD workflows, and deployment scripts.

• Produce technical specifications, architecture diagrams, and operational runbooks.
• Ensure alignment with enterprise security, data governance, and audit requirements.
• Contribute to enterprise AI patterns and reusable modules