Machine Learning Engineer

About the Job

Location

About Us

At Neural Nurture, we are committed to advancing the frontiers of Artificial Intelligence and Machine Learning. Our research lab focuses on solving some of the most exciting and challenging problems in Machine Learning, Natural Language Processing, and Computer Vision. 

Role Overview

As an ML Engineer, you will bridge the gap between research and production - building, optimizing, and deploying ML systems that power real-world products. You'll work closely with Applied Scientists and product teams to take models from prototype to scalable, reliable services. 

Areas of focus could be one or more of the following:

• ML Systems & Infrastructure

  : Building robust training and inference pipelines, model serving, and monitoring systems. 

• NLP Engineering

  : Deploying and optimizing language models for tasks such as conversational AI, information extraction, and search. 

• MLOps

  : Establishing CI/CD for ML, experiment tracking, model versioning, and automated retraining workflows. 

Responsibilities

• Design, build, and maintain end-to-end ML pipelines - from data ingestion and feature engineering to model training, evaluation, and deployment. 
• Optimize models for latency, throughput, and cost in production environments. 
• Collaborate with Applied Scientists to translate research prototypes into production-ready systems. 
• Build and maintain ML infrastructure including experiment tracking, model registries, and monitoring/alerting for model performance drift. 
• Develop APIs and services to expose ML capabilities to downstream applications. 
• Write clean, well-tested, and documented code following engineering best practices. 
• Troubleshoot and debug issues across the full ML stack - data, training, and serving. 

Qualifications

• Bachelor's or Master's in Computer Science, Machine Learning, or a related field. 
• Strong programming skills in Python. 
• Experience building and deploying ML models in production environments. 
• Proficiency with Deep Learning frameworks (PyTorch preferred). 
• Solid understanding of ML fundamentals - training, evaluation, overfitting, data leakage, etc. 
• Experience with ML pipeline/orchestration tools (e.g., Metaflow, Airflow, Kubeflow). 
• Familiarity with REST APIs, containerization (Docker), and cloud platforms (AWS/GCP). 
• Strong software engineering practices - version control (Git), testing, code review. 

Preferred Qualifications

• Experience with NLP systems (Transformers, LLM serving, RAG pipelines) or CV systems in production. 
• Familiarity with model optimization techniques - quantization, distillation, ONNX, TensorRT. 
• Experience with experiment tracking tools (MLflow, Weights & Biases). 
• Familiarity with Hugging Face ecosystem. 
• Experience with large-scale data processing (Spark, Dask, or similar). 
• Exposure to Kubernetes or other container orchestration platforms. 
• Prior experience with monitoring/observability for ML systems. 

Why Join Neural Nurture?

At Neural Nurture, you will work alongside experienced researchers and engineers specializing in ML, NLP, and Computer Vision. You'll have the opportunity to build systems that directly power AI products while staying close to cutting-edge research. Our collaborative environment values engineering excellence and provides hands-on mentorship, helping you grow as an ML professional with experience spanning both research-driven and production-grade systems. 

Compensation:

Base salary for this full-time position ranges from ₹3L to ₹5L, along with performance bonuses, equity, and benefits. Compensation is determined based on role, level, and location. Individual pay within the range is influenced by factors such as work location, relevant skills, experience, and educational background.