AI (GenAI) Engineer

• Posted 2 hours ago • Updated 2 hours ago

Full Time

On-site

Fitment

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Skills

Palo Alto
Computer Science
Electrical Engineering
Data Science
Python
SQL
Time Series
Machine Learning (ML)
Databricks
Apache Spark
Data Lake
Storage
DevOps
Git
Automated Testing
JSON
RBAC
Privacy
4G
Network Operations
Root Cause Analysis
Documentation
Workflow
Orchestration
Machine Learning Operations (ML Ops)
Knowledge Base
Analytics
Kubernetes
LangChain
LlamaIndex
Vector Databases
Regression Testing
Telecommunications
3GPP
Ericsson
Nokia
Microsoft Azure
Artificial Intelligence
Generative Artificial Intelligence (AI)
Network
Meta-data Management
Semantics
Caching
Continuous Integration
Continuous Delivery
Evaluation
Continuous Monitoring
Budget
KPI
Inventory
Dashboard
Collaboration
Data Engineering
RAN
Use Cases

Summary

Location: Palo Alto Office

AI (GenAI) Engineer - Job Requirements

Minimum qualifications

BS/MS in computer science, Electrical Engineering, Data Science, or a related technical field; or equivalent practical experience.
3-5 years of experience in AI/ML engineering, data engineering, or applied data science delivering production-grade solutions.
Strong Python and SQL skills; mastery of working with large-scale telemetry/time-series datasets and building reliable, testable data transformations.
Hands-on experience with Azure services for data/AI solutions, including:
- Azure Machine Learning; Azure AI Services/Azure OpenAI (LLM/GenAI capabilities)
- Azure Databricks/Spark (Delta Lake, lakehouse patterns)
- Azure Data Lake Storage / Blob Storage
- Azure Functions or similar serverless compute
- Azure DevOps (or similar CI/CD tooling), Git, and automated testing
Working knowledge of GenAI development patterns, including:
- Retrieval-Augmented Generation (RAG): chunking strategies, embeddings, hybrid search, re-ranking, grounding with citations, vector stores (e.g., Azure AI Search)
- Prompt design: system prompts, few-shot patterns, structured outputs (JSON/JSON Schema), function/tool calling
- Evaluation fundamentals: response quality, grounding, accuracy, latency, cost, and safety
Production mindset: robust logging/monitoring, tracing, observability, troubleshooting; security basics (RBAC, managed identities, Key Vault, data privacy/PII handling), and operational readiness (rate limits, retries, timeouts, backoff, caching).

Domain (RAN & Mobility) qualifications

Solid understanding of 4G/5G RAN and mobility concepts (e.g., handovers, drops, throughput, congestion, interference, PRB utilization, RSRP/RSRQ/SINR).
Ability to translate network issues into measurable KPIs and investigative workflows, producing actionable outputs for operations and engineering (e.g., RCA steps, remediation recommendations, and change validation plans).

Preferred qualifications

Built an internal assistant/copilot for network operations, triage, or RCA using KPIs, alarms, tickets, and documentation; experience grounding outputs with traceable evidence and citations.
Experience with agentic workflows and orchestration (function/tool calling, multi-step chains, retries/guardrails) to automate diagnosis and propose actions.
MLOps/LLMOps practices: CI/CD for pipelines/services, model/prompt/knowledge-base versioning, automated evaluations (e.g., RAG quality), drift monitoring, observability (OpenTelemetry), and cost/token controls.
Azure ecosystem depth: Azure AI Search (vector/hybrid search), Azure Event Hubs/Stream analytics, Azure Data Factory/Synapse pipelines, AKS (Kubernetes), and containerized deployments.
Familiarity with GenAI frameworks and tooling (e.g., Semantic Kernel, LangChain/LlamaIndex), MLflow/Model Registry, vector databases, and prompt/unit regression testing.
Understanding of telecom standards and tooling: 3GPP concepts, vendor-specific counters (e.g., Ericsson/Nokia/Samsung)
Relevant Azure certification (or in progress), especially Azure AI Engineer Associate.

What you'll do

Build, deploy, and operate GenAI-powered tools that accelerate network troubleshooting: triage assistants, KPI summaries, anomaly detection/explanations, and recommended next actions with citations to source data.
Design and implement RAG pipelines: document preparation (chunking/metadata), embeddings, vector search with re-ranking, grounding and citation strategies, semantic caching, and safety guardrails.
Ship reliable services: productionize models and prompts with CI/CD, automated tests, canary/A-B releases, monitoring/alerts, and SLOs for accuracy, grounding, latency, and cost.
Implement evaluation and continuous monitoring: offline and online eval harnesses, golden sets, human-in-the-loop review, prompt/knowledge drift detection, and token/cost budgets.
Integrate with internal systems and tools: alarms and KPI platforms, ticketing, inventory/topology APIs, runbooks, and dashboards to close the loop from detection to remediation.
Collaborate cross-functionally with data engineering, platform/security, and RAN SMEs to take use cases from discovery to production and iterate based on measurable impact (e.g., MTTR reduction, accuracy lift, fewer escalations).

Note: Job Description and Background Check

Candidates may be subjected to a Background Check /Drug Test as required by the end client before the assignment starts.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: RTL155990
Position Id: ead9628a1db76e7c01e09decd1232fdd
Posted 2 hours ago

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AI (GenAI) Engineer

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