Senior Machine Learning Engineer (Sangli)

About the RoleWe 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 .ResponsibilitiesMachine Learning & ModelingDesign 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 SystemsBuild and optimize LLM-based applications using techniques such as:Retrieval-Augmented Generation (RAG)Prompt engineering and prompt optimizationContext management and response evaluationUnderstand and mitigate challenges such as hallucinations, latency, and cost.Production & DeploymentDevelop 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 ReliabilityImplement 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 & ArchitectureDesign scalable architectures involving:MicroservicesEvent-driven pipelinesVector databases and retrieval systemsMake trade-offs between accuracy, latency, scalability, and cost.Collaboration & LeadershipCollaborate 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-makingRequired Qualifications4+ 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 learningModel evaluation and validationOverfitting, bias-variance trade-offsExperience 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 .QualificationsStrong 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 HaveHands-on experience deploying and operating LLMs in production, with awareness of limitations, evaluation, and cost implications .LLM + OCR + document AI, PDF parsing libraries experienceFamiliarity with retrieval-augmented generation (RAG), vector DBs .Monitoring/observability tools (CloudWatch, Prometheus, Grafana).Infrastructure-as-code (Terraform, Cloudformation etc).Familiarity with LangChain / LlamaIndexExperience with web crawlers or large-scale data ingestion.Morningstar is an equal opportunity employerMorningstar'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 perks 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 Apply on Kit Job: kitjob.in/job/4mhoh3