Client Albertsons Job Title Sr/Lead ML Engineer Placement type (FTE/C/CTH) C/CTH Duration 6 month with extension Location Phoenix AZ, must be onsite 5 days a week – LOCALS Start Date 2 weeks from the offer C2C Rate $55-60/hr W2 Or $65-70/hour on C2C Interview Process One and done Reason for position Integration ML to the Observability Grafana platform Visa GC, USC, H4ead (genuine) Team Overview Onshore and offshore Project Description AI/ML for Observability (AIOps) Developed machine learning and deep learning solutions for observability data to enhance IT operations. Implemented time series forecasting, anomaly detection, and event correlation models. Integrated LLMs using prompt engineering, fine-tuning, and RAG for incident summarization. Built MCP client-server architecture for seamless integration with the Grafana ecosystem. Duties/Day to Day Overview Machine Learning & Model Development • Design and develop ML/DL models for: • Time series forecasting (e.g., 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 frameworks like TensorFlow, PyTorch, or scikit-learn • Evaluate model performance using metrics like precision, recall, F1-score, and AUC ML Pipeline Engineering • Build scalable data pipelines for training and inference (batch or streaming) • Preprocess large observability datasets from tools like Prometheus, Kafka, or BigQuery • Deploy models using cloud-native services (e.g., GCP Vertex AI, Azure ML, Docker/Kubernetes) • Maintain retraining pipelines and monitor for 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 large language models • Integrate Retrieval-Augmented Generation (RAG) with vector databases (e.g., FAISS, Pinecone) • Control latency, hallucinations, and cost in production LLM pipelines Grafana & MCP Ecosystem Integration • Build or extend MCP client/server components for Grafana • Surface ML model outputs (e.g., anomaly scores, predictions) in observability dashboards • Collaborate with observability engineers to integrate ML insights into existing monitoring tools Collaboration & Agile Delivery • Participate in daily stand-ups, sprint planning, and retrospectives • Collaborate with: • Data engineers on pipeline performance and data ingestion • Frontend developers for real-time data visualizations • SRE and DevOps teams for alert tuning and feedback loop integration • Translate model outputs into actionable insights for platform teams Testing, Documentation & Version Control • Write unit, integration, and regression tests for ML code and pipelines • Maintain documentation on models, data sources, assumptions, and APIs • Use Git, CI/CD pipelines, and model versioning tools (e.g., MLflow, DVC) Top Requirements (Must haves) AI ML Engineer Skills Design and develop machine learning algorithms and deep learning applications and systems for Observability data (AIOps) Hands on experience in Time series forecasting/prediction, anomaly detection ML algorithms Hands on experience in event classification and correlation ML algorithms Hands on experience on integrating with LLMs with prompt/fine-tuning/rag for effective summarization Working knowledge on implementing MCP client and server for Grafana Eco-system or similar exposure Key Skills: • Programming languages: Python, R • ML Frameworks: TensorFlow, PyTorch, scikit-learn • Cloud platforms: Google Cloud,  Azure • Front-End Frameworks/Libraries: Experience with frameworks like React, Angular, or Vue.js, and libraries like jQuery. • Design Tools: Proficiency in design software like Figma, Adobe XD, or Sketch. • Databases: Knowledge of database technologies like MySQL, MongoDB, or PostgreSQL. • Server-Side Languages: Familiarity with server-side languages like Python, Node.js, or Java. • Version Control: Experience with Git and other version control systems. • Testing: Knowledge of testing frameworks and methodologies. • Agile Development: Experience with agile development methodologies. • Communication and Collaboration: Strong communication and collaboration skills. • Experience: Lead – 10 to 12 Years (Onshore and Offshore). Developers - 6 to 8 Years for Engineers