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
