Senior Machine Learning Engineer

About the Role

We are looking for a Senior Machine Learning Engineer to design, build, and scale production-grade ML and GenAI systems.

In 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.

This role requires a strong balance of machine learning expertise, software engineering practices, and real-world deployment experience.

Responsibilities

Machine Learning & Modeling

• Design and develop ML models for structured and unstructured data (classification, NLP, time series).
• Perform feature engineering, model selection, and hyperparameter tuning.
• Evaluate models using appropriate metrics (precision, recall, F1, ROC-AUC, latency, cost).

GenAI & LLM Systems

• Build and optimize LLM-based applications using techniques such as:
• Retrieval-Augmented Generation (RAG)
• Prompt engineering and prompt optimization
• Context management and response evaluation
• Understand and mitigate challenges such as hallucinations, latency, and cost.

Production & Deployment

• Develop and deploy scalable ML/LLM inference services using Python (FastAPI/Flask).
• Containerize applications using Docker and deploy on cloud platforms (AWS preferred).
• Build end-to-end pipelines from data ingestion → training → deployment → inference.

MLOps & System Reliability

• Implement CI/CD pipelines for ML workflows.
• Monitor model performance, detect data/model drift, and trigger retraining pipelines.
• Ensure reliability, scalability, and observability of ML systems (logs, metrics, alerts).

System Design & Architecture

• Design scalable architectures involving:
• Microservices
• Event-driven pipelines
• Vector databases and retrieval systems
• Make trade-offs between accuracy, latency, scalability, and cost.

Collaboration & Leadership

• Collaborate with data engineers, backend engineers, and product teams to productionize ML solutions.
• Mentor junior engineers and promote ML engineering best practices.
• Contribute to design reviews and technical decision-making

Required Qualifications

• 4+ years of experience in Machine Learning / Applied AI / ML Engineering roles.
• Strong programming skills in Python (ML + backend/API development).
• Hands-on experience building and deploying ML models in production environments.
• Solid understanding of ML concepts:
• Supervised/unsupervised learning
• Model evaluation and validation
• Overfitting, bias-variance trade-offs
• Experience with LLMs and GenAI applications (RAG, prompt engineering, evaluation).
• Experience with SQL databases (PostgreSQL).
• Experience with REST APIs, Docker, and cloud platforms (AWS preferred).
• Strong understanding of system design and scalable architecture.
• Good communication skills and a product-first mindset.

Qualifications

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  Strong programming skills in Python (APIs, pipelines, services).
• 5+ years experience in MLOps, backend engineering, data engineering or related roles.
• Good knowledge of ML principles (e.g. precision, recall, inference time, latency/throughput trade-offs).
• Solid knowledge of AWS services (Bedrock, Lambda, EKS, S3, etc).
• Experience with CI/CD pipelines, containerization (Docker/Kubernetes).
• Understanding of microservices architectures, queues/events, and scalability.
• Experience with SQL databases (PostgreSQL).
• Good communication skills and a product-first mindset.

Nice to Have

• Hands-on experience deploying and operating LLMs in production, with awareness of limitations, evaluation, and cost implications.
• LLM + OCR + document AI, PDF parsing libraries experience
• Familiarity with retrieval-augmented generation (RAG), vector DBs.
• Monitoring/observability tools (CloudWatch, Prometheus, Grafana).
• Infrastructure-as-code (Terraform, Cloudformation etc).
• Familiarity with LangChain / LlamaIndex
• Experience with web crawlers or large-scale data ingestion.

Morningstar is an equal opportunity employer

Morningstars hybrid work environment gives you the opportunity to collaborate in-person each week as weve found that were at our best when were purposely together on a regular basis. In most of our locations, our hybrid work model is four days in-office each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, youll have tools and resources to engage meaningfully with your global colleagues.

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