Role Overview The AI/ML Engineer is the intelligence core of the AI Ops platform. You will design, build, train, and deploy the predictive models that drive autonomous infrastructure management - including failure forecasting, anomaly detection, capacity modelling, and root cause analysis. Beyond model development, you will own the MLOps pipeline, the agentic decision engine, the continuous learning loop, and the model registry - ensuring the platform's AI capabilities improve with every resolved incident and learned pattern.

Key Responsibilities Design and train the Failure Forecast model targeting 94%+ accuracy at a 6-hour prediction horizon Build the Anomaly Detection model targeting 97%+ precision for real-time infrastructure signal classification Develop the Capacity Model for 24-hour resource demand forecasting at 91%+ accuracy Build and tune the Root Cause AI model targeting 89%+ average confidence on incident attribution Design and implement the agentic decision engine - the Detect Predict Decide Heal loop that drives auto-remediation Build and maintain the MLOps pipeline: data ingestion, feature engineering, model training, validation, and deployment Implement the Continuous Learning module: automated retraining triggers, knowledge base indexing, and runbook auto-generation Develop the model registry API layer exposing real-time accuracy scores and model metadata to the platform Integrate AI outputs with the Incident Control backend for root cause enrichment and resolution recommendation Design the Digital Twin data model for real-time infrastructure state representation Monitor model performance in production and implement drift detection and alerting Collaborate with Backend Engineers on inference API design for sub-100ms prediction latency EXPECTATIONS Achieve and maintain model accuracy targets: 94% (Failure Forecast), 97% (Anomaly), 91% (Capacity), 89% (Root Cause)



Deliver a fully operational agentic loop processing 47,600+ AI decisions per day from go-live Ensure model inference endpoints respond within 100ms on p95 for all production predictions Keep human escalation rate below 2.5% through high-confidence autonomous decision-making Implement a continuous learning pipeline that indexes and learns from every resolved incident Produce explainable AI outputs with confidence scores on all predictions surfaced in the UI Document all model architectures, training datasets, evaluation metrics, and retraining schedules Skills &

Competencies Technical Skills Deep expertise in supervised and unsupervised machine learning: anomaly detection, classification, regression, time-series forecasting Proficiency in Python ML stack: scikit-learn, XGBoost, LightGBM, PyTorch or TensorFlow Time-series modelling: LSTM, Transformer-based models, Prophet, or ARIMA for infrastructure metric forecasting MLOps platform experience: MLflow, Kubeflow, Weights & Biases, or equivalent for experiment tracking and model management Feature engineering from infrastructure telemetry data: metrics, logs, traces, and events Experience building and deploying inference APIs at low-latency production scale Knowledge of LLM integration for runbook generation and knowledge base indexing GPU compute management for model training on cloud platforms (AWS SageMaker, Azure ML, or GCP Vertex AI) Familiarity with reinforcement learning or agentic AI patterns for autonomous remediation design Statistical proficiency: hypothesis testing, confidence intervals, distribution analysis Functional & Soft Skills Ability to translate complex model behaviour into transparent, confidence-scored outputs for operations teams Strong experimental mindset - rigorous about evaluation metrics and avoiding overfitting Collaborative engineering partner to Backend and DevOps engineers on pipeline integration Clear documentation practices for model cards, training pipelines.

📌 AI / ML Engineer (Bengaluru)
🏢 Accenture in India
📍 Bengaluru