Machine Learning Engineer
New Relic
Machine Learning Engineer
Job Description
About This Role
As a Machine Learning Engineer at New Relic in Bangalore, you will contribute across the entire ML lifecycle, building production-ready inference pipelines, deploying and versioning models, and implementing continuous validation for the observability platform. Focus on LLM tasks like fine-tuning generative AI models, designing agentic language chains, and prototyping recommender systems for conversational AI bots and RAG systems. This role combines operational and engineering aspects to drive AI-driven solutions in an AI-first world.
What You'll Do
- Fine-tune generative AI models to enhance performance
- Design AI agents for conversational AI applications
- Experiment with new techniques to develop models for observability use cases
- Build and maintain inference pipelines for efficient model deployment
- Manage deployment and model versioning pipelines for seamless updates
- Develop tooling to continuously validate models in production environments
- Contribute to conversational AI bots, RAG systems, and traditional ML problem solving
What We're Looking For
- 5+ years of developing production-grade applications in Python
- Proven track record of deploying and managing ML models in production environments
- Proficiency in software engineering design practices
- Experience working with transformer models and text embeddings
- Proficiency in Kubernetes and containers
- Familiarity with common ML/NLP libraries such as PyTorch, Tensorflow, HuggingFace Transformers, and SpaCy
Bonus Points If You Have
- Familiarity with the AWS ecosystem
- Past projects involving the construction of agentic language chains
Key Skills
Locations
- Bangalore, India, United States
Salary
25,000,000 - 55,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Pythonintermediate
- C++intermediate
- Kotlinintermediate
- PyTorchintermediate
- Tensorflowintermediate
- HuggingFace Transformersintermediate
- SpaCyintermediate
- Kubernetesintermediate
- sklearnintermediate
- kubeflowintermediate
Required Qualifications
- 5+ years of developing production-grade applications in Python (experience)
- Proven track record of deploying and managing ML models in production environments (experience)
- Proficiency in software engineering design practices (experience)
- Experience working with transformer models and text embeddings (experience)
- Proficiency in Kubernetes and containers (experience)
- Familiarity with common ML/NLP libraries such as PyTorch, Tensorflow, HuggingFace Transformers, and SpaCy (experience)
Preferred Qualifications
- Familiarity with the AWS ecosystem (experience)
- Past projects involving the construction of agentic language chains (experience)
Responsibilities
- Fine-tune generative AI models to enhance performance
- Design AI agents for conversational AI applications
- Experiment with new techniques to develop models for observability use cases
- Build and maintain inference pipelines for efficient model deployment
- Manage deployment and model versioning pipelines for seamless updates
- Develop tooling to continuously validate models in production environments
- Contribute to conversational AI bots, RAG systems, and traditional ML problem solving
Tags & Categories
Machine Learning Engineer
New Relic
Machine Learning Engineer
Job Description
About This Role
As a Machine Learning Engineer at New Relic in Bangalore, you will contribute across the entire ML lifecycle, building production-ready inference pipelines, deploying and versioning models, and implementing continuous validation for the observability platform. Focus on LLM tasks like fine-tuning generative AI models, designing agentic language chains, and prototyping recommender systems for conversational AI bots and RAG systems. This role combines operational and engineering aspects to drive AI-driven solutions in an AI-first world.
What You'll Do
- Fine-tune generative AI models to enhance performance
- Design AI agents for conversational AI applications
- Experiment with new techniques to develop models for observability use cases
- Build and maintain inference pipelines for efficient model deployment
- Manage deployment and model versioning pipelines for seamless updates
- Develop tooling to continuously validate models in production environments
- Contribute to conversational AI bots, RAG systems, and traditional ML problem solving
What We're Looking For
- 5+ years of developing production-grade applications in Python
- Proven track record of deploying and managing ML models in production environments
- Proficiency in software engineering design practices
- Experience working with transformer models and text embeddings
- Proficiency in Kubernetes and containers
- Familiarity with common ML/NLP libraries such as PyTorch, Tensorflow, HuggingFace Transformers, and SpaCy
Bonus Points If You Have
- Familiarity with the AWS ecosystem
- Past projects involving the construction of agentic language chains
Key Skills
Locations
- Bangalore, India, United States
Salary
25,000,000 - 55,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Pythonintermediate
- C++intermediate
- Kotlinintermediate
- PyTorchintermediate
- Tensorflowintermediate
- HuggingFace Transformersintermediate
- SpaCyintermediate
- Kubernetesintermediate
- sklearnintermediate
- kubeflowintermediate
Required Qualifications
- 5+ years of developing production-grade applications in Python (experience)
- Proven track record of deploying and managing ML models in production environments (experience)
- Proficiency in software engineering design practices (experience)
- Experience working with transformer models and text embeddings (experience)
- Proficiency in Kubernetes and containers (experience)
- Familiarity with common ML/NLP libraries such as PyTorch, Tensorflow, HuggingFace Transformers, and SpaCy (experience)
Preferred Qualifications
- Familiarity with the AWS ecosystem (experience)
- Past projects involving the construction of agentic language chains (experience)
Responsibilities
- Fine-tune generative AI models to enhance performance
- Design AI agents for conversational AI applications
- Experiment with new techniques to develop models for observability use cases
- Build and maintain inference pipelines for efficient model deployment
- Manage deployment and model versioning pipelines for seamless updates
- Develop tooling to continuously validate models in production environments
- Contribute to conversational AI bots, RAG systems, and traditional ML problem solving
Tags & Categories