Kforce Inc hiring 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 (GCP 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: Py...