AI Engineer – Level II

Globenet Consulting Corp

16h•Onsite

About The Position

Join a high-impact AI team building secure, scalable GenAI systems. Gain exposure to: Cutting-edge RAG and agentic AI architectures Azure and AWS AI ecosystems Multi-modal LLM integration across vision and speech Production-grade CI/CD for AI/ML workloads Fast-tracked certifications and career growth As an AI Engineer (Level II), you’ll design, implement, and optimize enterprise-scale AI systems. You’ll lead architecture, agent orchestration, and model integration while collaborating with cross-functional teams to deliver production-ready solutions.

Requirements

5+ years in software engineering
2+ years in GenAI/LLM systems
Strong CS fundamentals: distributed systems, algorithms, concurrency, networking
SDLC excellence: clean architecture, SOLID principles, testing frameworks
Secure development: input validation, secret hygiene, sandboxing
Performance tuning: latency optimization, vector index profiling
Expertise in RAG, embeddings, transformer models, and multi-modal pipelines
Production-level C#, Python, .NET; TypeScript for service/UI (as needed)
Experience with Azure and AWS AI tools and operations
Familiarity with fine-tuning, safety tooling, model traceability
Strong delivery skills: architecture, stakeholder alignment, roadmap execution
Microsoft Certified: Azure AI Fundamentals (AI-900)
Microsoft Certified: Azure Data Fundamentals (DP-900)
Responsible AI certification
AWS Machine Learning Specialty
TensorFlow Developer
Kubernetes CKA/CKAD
SAFe Agile Software Engineering

Nice To Haves

Azure AI Engineer (AI-102), Data Scientist (DP-100), Architect (AZ-305), or Developer (AZ-204)
Experience with MLflow, Hugging Face, vector tuning (HNSW/IVF)
Responsible AI playbooks, incident response frameworks
CI/CD for AI (Azure DevOps, AWS CodePipeline), hybrid deployments (Azure Arc, AWS Outposts)

Responsibilities

Design RAG pipelines using Azure AI/Search, Redis, FAISS, HNSW
Build conversational systems with prompt lifecycle management and telemetry
Integrate LLMs like Azure OpenAI, Claude, Llama, and open-source models
Deploy Model Context Protocol (MCP) servers with RBAC and audit trails
Implement Azure AI Agent Service patterns for agent registry and policy enforcement
Use Azure Batch and AWS EMR for scalable inferencing and processing
Build ingestion pipelines with PII redaction, metadata enrichment, SLA tracking
Operate vectorization pipelines with quality gates and drift detection
Leverage ADF, Databricks, and EMR for scalable workflows
Orchestrate multi-agent workflows using Semantic Kernel, AutoGen, CrewAI, LangChain
Apply governance and lifecycle management for agent runtimes
Fine-tune models, conduct A/B testing, and implement CI/CD pipelines with validation