AI/ML Engineer (ONLY W2)

Duties / Day-to-Day Responsibilities: 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 ML Pipeline Engineering • Build scalable training and inference pipelines (batch or streaming) • 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 Grafana & MCP Ecosystem Integration • 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 Collaboration & Agile Delivery • Participate in daily stand-ups, sprint planning, and retrospectives • Work with data engineers on pipeline performance and data ingestion • Collaborate with frontend developers for real-time visualizations • Partner with SRE and DevOps teams for alert tuning and feedback integration • Translate ML 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 (MLflow, DVC) Top Requirements / Must-Have Skills: • 6- 8 years Design and develop 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