Al Ml Engineer Pune (India)

About The Chance A rapid-scaling AI/ML solutions partner in the enterprise tech sector, we architect and deploy production-grade machine learning systems for global clients across fintech, healthcare, and logistics. Our engineers solve high-impact business problems using cutting-edge ML modelsfrom recommendation engines and anomaly detection to NLP-driven automationdeployed via scalable cloud infrastructure and MLOps pipelines. Role & Responsibilities Design, train, and deploy supervised/unsupervised ML models using Python, scikit-learn, TensorFlow, or PyTorch to solve real-world client problems. Build end-to-end data pipelinesfrom ingestion to feature engineering to model servingusing pandas, Spark, or Dask for large-scale datasets. Collaborate with data engineers and product teams to integrate ML models into production systems via REST APIs or containerized services (Docker/K8s). Optimize model performance through hyperparameter tuning, cross-validation, and A/B testing frameworks to ensure business impact. Implement MLOps best practices: model versioning (MLflow), monitoring (Prometheus/Grafana), and CI/CD automation for model retraining. Translate business KPIs into ML evaluation metrics and communicate technical outcomes to non-technical stakeholders. Skills & Qualifications Must-Have Python scikit-learn TensorFlow PyTorch pandas SQL REST API Git Preferred MLflow Docker Kubernetes Perks & Culture Highlights Work with Fortune 500 clients and tackle mission-critical AI problems from day one. Access to cutting-edge tech stack and hands-on project ownership in agile, cross-functional teams. Opportunities for upskilling via internal AI labs, certification sponsorships, and conference participation. Skills: ml,pandas,docker,python,pipelines About The Chance A rapid-scaling AI/ML solutions partner in the enterprise tech sector, we architect and deploy production-grade machine learning systems for global clients across fintech, healthcare, and logistics. Our engineers solve high-impact business problems using cutting-edge ML modelsfrom recommendation engines and anomaly detection to NLP-driven automationdeployed via scalable cloud infrastructure and MLOps pipelines. Role & Responsibilities Design, train, and deploy supervised/unsupervised ML models using Python, scikit-learn, TensorFlow, or PyTorch to solve real-world client problems. Build end-to-end data pipelinesfrom ingestion to feature engineering to model servingusing pandas, Spark, or Dask for large-scale datasets. Collaborate with data engineers and product teams to integrate ML models into production systems via REST APIs or containerized services (Docker/K8s). Optimize model performance through hyperparameter tuning, cross-validation, and A/B testing frameworks to ensure business impact. Implement MLOps best practices: model versioning (MLflow), monitoring (Prometheus/Grafana), and CI/CD automation for model retraining. Translate business KPIs into ML evaluation metrics and communicate technical outcomes to non-technical stakeholders. Skills & Qualifications Must-Have Python scikit-learn TensorFlow PyTorch pandas SQL REST API Git Preferred MLflow Docker Kubernetes Perks & Culture Highlights Work with Fortune 500 clients and tackle mission-critical AI problems from day one. Access to cutting-edge tech stack and hands-on project ownership in agile, cross-functional teams. Opportunities for upskilling via internal AI labs, certification sponsorships, and conference participation. Skills: ml,pandas,docker,python,pipelines