AI (GenAI) Engineer

MACHINE LEARNING TECHNOLOGIES LLC; Palo Alto (CA); On-site; USD 68,880,000 - 123,984,000; Full time

Job summary

MACHINE LEARNING TECHNOLOGIES LLC is seeking an AI (GenAI) Engineer for their Palo Alto location. The position involves building, deploying, and integrating GenAI-powered tools to enhance network operations and troubleshooting. The ideal candidate will have a strong background in AI, Python, SQL, and experience with Azure services.

Preferred educational qualifications include a BS/MS in Computer Science, and the candidate should have 3-5 years in AI/ML engineering. The role requires collaboration with cross-functional teams and effective communication skills.

Qualifications

• 3-5 years of experience in AI/ML engineering or data engineering.
• Strong Python and SQL skills, experience with time-series datasets.
• Hands-on experience with Azure services for data/AI solutions.

Responsibilities

• Build and deploy GenAI-powered tools for network troubleshooting.
• Design and implement RAG pipelines for document preparation.
• Ship reliable services by productionizing models and prompts.

Skills

Python

SQL

GenAI Development

Azure Services

Education

BS/MS in Computer Science or related field

Tools

Azure Machine Learning

Azure Databricks

Azure Functions

Job description

Job Title

AI (GenAI) Engineer

Job Details

• Job ID: J53175
• Location: Palo Alto, CA
• Duration: 15 Months + Extension
• Hourly Rate: Depending on Experience (DOE)
• Work Authorization: US Citizen, Green Card, OPT-EAD, CPT, H-1B, H4-EAD, L2-EAD, GC-EAD
• Client: To Be Discussed Later
• Employment Type: W-2, 1099, C2C

Responsibilities

• 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).

Working Knowledge Of GenAI Development Patterns

• 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-based 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, Lang Chain/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.

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 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

Equal Opportunity Employer

MACHINE LEARNING TECHNOLOGIES LLC is an equal opportunity employer inclusive of female, minority, disability and veterans, (M/F/D/V). Hiring, promotion, transfer, compensation, benefits, discipline, termination and all other employment decisions are made without regard to race, color, religion, sex, sexual orientation, gender identity, age, disability, national origin, citizenship/immigration status, veteran status or any other protected status. MACHINE LEARNING TECHNOLOGIES LLC will not make any posting or employment decision that does not comply with applicable laws relating to labor and employment, equal opportunity, employment eligibility requirements or related matters. Nor will MACHINE LEARNING TECHNOLOGIES LLC require in a posting or otherwise U.S. citizenship or lawful permanent residency in the U.S. as a condition of employment except as necessary to comply with law, regulation, executive order, or federal, state, or local government contract.