Role: AI Engineer Level I
Locations: Washington, DC - onsite
Position Summary 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.
Key Responsibilities 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.
Core Engineering Skills - 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.
Tech Stack Exposure 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
Qualifications 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
Certifications (Required for Level I)
- 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)
Required Skills - 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
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