AI Engineer – Level I

Role: AI Engineer Level I
Locations: Washington DC - onsite

As an entry-level AI Engineer you will support the development of scalable secure AI systems with a focus on Retrieval-Augmented Generation (RAG) agentic AI and cloud-based infrastructure. You will work under guidance to implement foundational components contribute to data pipelines and gain hands-on experience with Azure and AWS technologies.

Support AI Solution Development

• Assist in building RAG pipelines using Azure AI/Search and vector DBs (e.g. Redis FAISS).
• Participate in developing conversational AI features: chunking embedding re-ranking citation formatting.
• Collaborate on integrating multi-modal models (Azure OpenAI OSS LLMs) with prompt routing and basic guardrails.

AI Infrastructure Integration

• Learn to deploy Model Context Protocol (MCP) servers and implement RBAC audit trails and validation mechanisms.
• Contribute to agent orchestration patterns using Azure AI Agent Service gaining exposure to policy enforcement.

Data Pipeline Contribution

• Support ingestion and ETL/ELT processes: document normalization metadata tagging PII redaction.
• Use Azure Data Factory and Databricks for scalable orchestrated data processing workflows.

Model Operations & Optimization

• Assist in model evaluations safety checks and offline testing suites.
• Participate in implementing CI/CD pipelines with basic security scans and performance logging.

• Familiar with CS fundamentals: algorithms data structures distributed systems.
• Exposure to SDLC best practices: clean code SOLID principles testing patterns.
• Awareness of secure coding principles and performance optimization techniques.

Azure: Azure OpenAI AI/Search AML Functions Key Vault ADF Databricks
AWS: SageMaker Bedrock Lambda API Gateway S3 EMR
Vector DBs: Azure AI Search Redis FAISS
Frameworks: Semantic Kernel AutoGen LangChain (beginner level)
Local Inference: Docker/Ollama for running small LLMs

Education: Bachelor s in CS Engineering Data Science or equivalent hands-on learning
Experience: 2 years in software engineering with exposure to GenAI concepts and cloud services

• Microsoft Certified: Azure AI Fundamentals (AI-900)
• Microsoft Certified: Azure Data Fundamentals (DP-900)
• Responsible AI awareness or certification
• AWS Machine Learning Specialty (preferred for Level I)
• TensorFlow Developer Kubernetes CKA/CKAD (plus)

• Understanding of RAG workflows embeddings vector databases
• Basic implementation of agent orchestration and prompt management
• Proficient in Python and C# for backend development
• Exposure to LLM integration fine-tuning and safety evaluation
• Comfortable working in Agile teams with cross-functional collaboration

Ready to grow your AI career Apply now and contribute to impactful enterprise AI solutions