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

• 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
Morningstar'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. 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, you'll have tools and resources to engage meaningfully with your global colleagues.
I10_MstarIndiaPvtLtd Morningstar India Private Ltd. (Delhi) Legal Entity
Morningstar'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. 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, you'll have tools and resources to engage meaningfully with your global colleagues.
I10_MstarIndiaPvtLtd Morningstar India Private Ltd. (Delhi) Legal Entity