AI/ML Engineer

RESPONSIBILITIES:
Kforce has a client that is seeking an AI/ML Engineer in Phoenix, AZ.

Duties/Day-to-Day Responsibilities:
* Develop machine learning and deep learning solutions for observability data to enhance IT operations
* Implement time series forecasting, anomaly detection, and event correlation models
* Integrate LLMs using prompt engineering, fine-tuning, and RAG for incident summarization
* Build MCP client-server architecture for seamless integration with the Grafana ecosystem
* The project also focuses on predicting emissions using ML models and enhancing observability through dynamic dashboards

Machine Learning & Model Development:
Design and develop ML/DL models for:
* Time series forecasting (system load, CPU/memory usage)
* Anomaly detection in logs, metrics, or traces
* Event classification and correlation to reduce alert noise
* Select, train, and tune models using TensorFlow, PyTorch, or scikit-learn
* Evaluate model performance with precision, recall, F1-score, and AUC
* Preprocess large observability datasets (Prometheus, Kafka, BigQuery)
* Deploy models using cloud-native services (Google Cloud Platform Vertex AI, Azure ML, Docker/Kubernetes)
* Maintain retraining pipelines and monitor model drift

LLM Integration for Observability Intelligence:
* Implement LLM-based workflows for summarizing incidents or logs
* Develop and refine prompts for GPT, LLaMA, or other LLMs
* Integrate Retrieval-Augmented Generation (RAG) with vector databases (FAISS, Pinecone)
* Control latency, hallucinations, and cost in production LLM pipelines
* Build or extend MCP client/server components for Grafana
* Surface ML outputs (anomaly scores, predictions) in dashboards
* Collaborate with observability engineers to integrate ML insights into monitoring tools
* Work with data engineers on pipeline performance and data ingestion
* Translate ML outputs into actionable insights for platform teams

REQUIREMENTS:
* 6- 8 years of designing and developing ML algorithms and DL applications for observability data (AIOps)
* Hands-on experience in time series forecasting, anomaly detection, and event classification
* Experience integrating LLMs with prompt engineering, fine-tuning, and RAG
* Working knowledge of MCP client and server development for Grafana or similar
* Programming: Python, R
* ML Frameworks: TensorFlow or PyTorch, scikit-learn
* Cloud Platforms: Google Cloud and/or Azure
* Front-End: React or Angular or Vue.js, or jQuery
* Design Tools: Figma or Adobe XD or Sketch
* Databases: MySQL or MongoDB or PostgreSQL
* Server-Side Languages: Python or Node.js or Java
* Version Control: Git and related systems
* Testing: Familiarity with testing frameworks and methodologies
* Development Methodologies: Agile
* Soft Skills: Strong communication and collaboration

The pay range is the lowest to highest compensation we reasonably in good faith believe we would pay at posting for this role. We may ultimately pay more or less than this range. Employee pay is based on factors like relevant education, qualifications, certifications, experience, skills, seniority, location, performance, union contract and business needs. This range may be modified in the future.

We offer comprehensive benefits including medical/dental/vision insurance, HSA, FSA, 401(k), and life, disability & ADD insurance to eligible employees. Salaried personnel receive paid time off. Hourly employees are not eligible for paid time off unless required by law. Hourly employees on a Service Contract Act project are eligible for paid sick leave.

Note: Pay is not considered compensation until it is earned, vested and determinable. The amount and availability of any compensation remains in Kforce's sole discretion unless and until paid and may be modified in its discretion consistent with the law.

This job is not eligible for bonuses, incentives or commissions.

Kforce is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status.

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