New Relic: Machine Learning Engineer

Job Description:

As a Machine Learning Engineer at New Relic, you’ll join a global team of innovators shaping the future of AI-driven observability. This role sits at the intersection of machine learning, software engineering, and cloud infrastructure — enabling you to design and deploy intelligent models that enhance the performance, automation, and insight of New Relic’s world-class observability platform. You’ll work across the entire ML lifecycle, from data preparation and model training to deployment and continuous validation, ensuring real-time accuracy and scalability in production environments.

This position offers the chance to work hands-on with conversational AI, RAG systems, and generative models, helping businesses monitor, optimize, and predict digital performance with greater precision. You’ll fine-tune large language models (LLMs), design intelligent agent chains, and implement AI-driven recommendations that push the boundaries of what’s possible in the observability domain.

At New Relic, you’ll be part of an inclusive, forward-thinking engineering culture that values creativity, curiosity, and collaboration. This is your opportunity to leverage the latest tools in AI, Kubernetes, and distributed systems while helping leading enterprises unlock the full potential of their data in an AI-first world.

Roles & Responsibilities:

• Design, build, and optimize machine learning inference pipelines to support large-scale, real-time applications.

• Deploy, version, and monitor ML models in production, ensuring high availability, accuracy, and efficiency.

• Collaborate with cross-functional teams to integrate ML models with New Relic’s observability platform and deliver AI-driven insights.

• Fine-tune and optimize generative AI and transformer-based models for conversational AI and RAG system enhancements.

• Develop agentic AI chains to improve automation, intelligent decision-making, and system reliability.

• Implement continuous validation and monitoring frameworks for ML models to ensure long-term performance.

• Manage model deployment infrastructure using Kubernetes, Docker, and CI/CD pipelines.

• Utilize and contribute to ML workflow orchestration systems such as Kubeflow, Argo, and Airflow.

• Conduct experiments and POCs to explore new AI techniques and observability-focused ML applications.

• Collaborate with data scientists and DevOps engineers to maintain efficient, scalable, and secure data pipelines.

• Document processes and share best practices for ML development, deployment, and maintenance within the engineering community.

Requirements & Eligibility:

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.

• Minimum of 5 years’ experience in software engineering or applied machine learning, with strong coding skills in Python, C++, or Kotlin.

• Deep expertise in transformer models, embeddings, and generative AI fine-tuning.

• Proficiency with ML/NLP frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, and SpaCy.

• Experience building and deploying ML models in production environments, including containerized solutions using Kubernetes.

• Solid understanding of distributed systems, message brokers (Kafka, RabbitMQ), and scalable architectures.

• Hands-on experience with ML workflow tools such as Airflow, Kubeflow, Sagemaker, or Seldon.

• Familiarity with cloud ecosystems (AWS, GCP, or Azure) and infrastructure-as-code tools.

• Strong knowledge of observability, monitoring practices, and data instrumentation.

• Excellent problem-solving abilities, attention to detail, and communication skills to collaborate effectively across teams.

• Bonus: Prior experience designing agentic language models or working with RAG pipelines for AI applications.

Expected Salary:

For a Machine Learning Engineer at New Relic in Bangalore, the estimated annual compensation ranges between ₹35 LPA to ₹60 LPA, depending on experience, technical proficiency, and expertise in cloud-native and AI technologies. Candidates with deep knowledge of LLMs, Kubernetes, and production ML systems can expect offers on the higher end of the scale.

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