Senior Machine Learning Engineer

About the Role\nWe are looking for a Senior Machine Learning Engineer to design, build, and scale production-grade ML and GenAI systems .\nIn this role, you will own the end-to-end lifecycle of ML solutions — from problem formulation and model development to deployment, monitoring, and continuous improvement . You will play a key role in building LLM-powered applications and scalable ML systems that power critical business use cases, including ESG analytics.\nThis role requires a strong balance of machine learning expertise, software engineering practices, and real-world deployment experience .\n\nResponsibilities\nMachine Learning & Modeling\nDesign and develop ML models for structured and unstructured data (classification, NLP, time series).\nPerform feature engineering, model selection, and hyperparameter tuning.\nEvaluate models using appropriate metrics (precision, recall, F1, ROC-AUC, latency, cost).\nGenAI & LLM Systems\nBuild and optimize LLM-based applications using techniques such as:\nRetrieval-Augmented Generation (RAG)\nPrompt engineering and prompt optimization\nContext management and response evaluation\nUnderstand and mitigate challenges such as hallucinations, latency, and cost.\nProduction & Deployment\nDevelop and deploy scalable ML/LLM inference services using Python (FastAPI/Flask).\nContainerize applications using Docker and deploy on cloud platforms (AWS preferred).\nBuild end-to-end pipelines from data ingestion → training → deployment → inference.\nMLOps & System Reliability\nImplement CI/CD pipelines for ML workflows.\nMonitor model performance, detect data/model drift , and trigger retraining pipelines.\nEnsure reliability, scalability, and observability of ML systems (logs, metrics, alerts).\nSystem Design & Architecture\nDesign scalable architectures involving:\nMicroservices\nEvent-driven pipelines\nVector databases and retrieval systems\nMake trade-offs between accuracy, latency, scalability, and cost.\nCollaboration & Leadership\nCollaborate with data engineers, backend engineers, and product teams to productionize ML solutions.\nMentor junior engineers and promote ML engineering best practices.\nContribute to design reviews and technical decision-making\n\nRequired Qualifications\n4+ years of experience in Machine Learning / Applied AI / ML Engineering roles.\nStrong programming skills in Python (ML + backend/API development).\nHands-on experience building and deploying ML models in production environments.\nSolid understanding of ML concepts:\nSupervised/unsupervised learning\nModel evaluation and validation\nOverfitting, bias-variance trade-offs\nExperience with LLMs and GenAI applications (RAG, prompt engineering, evaluation).\nExperience with SQL databases (PostgreSQL).\nExperience with REST APIs, Docker, and cloud platforms (AWS preferred).\nStrong understanding of system design and scalable architecture.\nGood communication skills and a product-first mindset .\n\nQualifications\nStrong programming skills in Python (APIs, pipelines, services).\n5+ years' experience in MLOps, backend engineering, data engineering or related roles.\nGood knowledge of ML principles (e.g. precision, recall, inference time, latency/throughput trade-offs).\nSolid knowledge of AWS services (Bedrock, Lambda, EKS, S3, etc).\nExperience with CI/CD pipelines , containerization (Docker/Kubernetes).\nUnderstanding of microservices architectures, queues/events, and scalability .\nExperience with SQL databases (PostgreSQL).\nGood communication skills and a product-first mindset .\n\nNice to Have\nHands-on experience deploying and operating LLMs in production , with awareness of limitations, evaluation, and cost implications .\nLLM + OCR + document AI, PDF parsing libraries experience\nFamiliarity with retrieval-augmented generation (RAG), vector DBs .\nMonitoring/observability tools (CloudWatch, Prometheus, Grafana).\nInfrastructure-as-code (Terraform, Cloudformation etc).\nFamiliarity with LangChain / LlamaIndex\nExperience with web crawlers or large-scale data ingestion.\n\nMorningstar is an equal opportunity employer\n\nMorningstar's hybrid work environment gives you the opportunity to collaborate in-person each week as we've found that we're at our best when we're purposely together on a regular basis.