AI Engineer – Level III

Globenet Consulting Corp

4h•Onsite

About The Position

As a Level III AI Engineer, you’ll own the architecture and execution of secure, scalable AI systems. You will lead technical delivery across RAG pipelines, agent frameworks, model ops, and cloud-based ML workflows. This role demands deep hands-on expertise and cross-functional leadership.

Requirements

8+ years in software engineering
2+ years in applied GenAI
Deep CS knowledge: distributed systems, concurrency, performance tuning
Expert in SDLC: clean architecture, SOLID, layered testing, DevSecOps practices
Secure AI app delivery: sandboxed tools, secrets hygiene, token/cost profiling
Agile leadership: drive sprints, lead technical planning, manage RACI across teams
Proficient in GenAI systems: embeddings, transformer models, vector DB indexing
Production-level expertise in Python, C#, .NET, and TypeScript (as needed)
Hands-on with Azure and AWS tools: AML, AKS, Databricks, SageMaker, EMR, EKS
Strong in model traceability, safety tooling, fine-tuning, and runtime observability
Strategic execution: solution architecture, roadmap alignment, delivery metrics
Azure AI Fundamentals (AI-900), Data Fundamentals (DP-900)
Responsible AI certifications
AWS Machine Learning Specialty
TensorFlow Developer
Kubernetes CKA or CKAD
SAFe Agile Software Engineering

Nice To Haves

Azure AI Engineer (AI-102)
Azure Data Scientist (DP-100)
Azure Solutions Architect (AZ-305)
Azure Developer Associate (AZ-204)

Responsibilities

Lead design of RAG pipelines using Azure AI Search, Redis, FAISS, HNSW
Deliver multi-turn conversational systems with prompt lifecycle, telemetry, and guardrails
Integrate LLMs (Azure OpenAI, Claude, Llama, OSS) with dynamic routing for cost/safety balance
Deploy MCP servers with RBAC, audit logging, version control, and rate limiting
Implement agent frameworks using Azure AI Agent Service (registry, policy enforcement, telemetry)
Operate large-scale inferencing via Azure Batch and AWS EMR
Lead ingestion pipelines: document normalization, metadata tagging, PII redaction, SLA/SLO tracking
Operate vectorization workflows with drift detection and quality gates
Architect scalable data flows using ADF, Databricks, and EMR
Orchestrate multi-agent systems with Semantic Kernel, AutoGen, CrewAI, LangChain, Agno
Govern agent runtimes using MCP controls for security and traceability
Evaluate and fine-tune models; run A/B testing and latency-cost analysis
Build secure CI/CD pipelines with integrated testing, scans, and trace logging
Enforce DevSecOps and AI threat modeling for LLM workloads