Job Description

As a crucial member of our team, you'll play a pivotal role across the entire machine learning lifecycle, contributing to our strategic Agentic AI platform, Conversational AI bots, RAG platform and traditional ML problem solving for our observability platform. Your tasks will encompass both science, engineering and operational aspects, including building production-ready inference pipelines, deploying and versioning models, and implementing continuous validation processes. On the LLM side you'll fine-tune generative AI models, design agentic language chains, and prototype recommender system experiments. Additionally, you will lead and guide the other Senior and Junior ML Engineers and assist the team's manager in the team-level tasks.



What you'll do


Lead end-to-end development of Agentic AI Platform service. Building and maintaining inference pipelines for efficient model deployment. Experimenting with new techniques of ML models for observability use cases. Managing deployment and model versioning pipelines for seamless updates. Fine-tuning generative AI models to enhance performance. Designing AI Agents for conversational AI applications. Developing tooling to continuously evaluate models in production environments.

Must have


8+ Years of overall experience Bachelor's or advanced degree in Computer Science, Engineering, Mathematics, or a related field. Advanced degree (Master's or Ph.D.) preferred. Demonstrated proficiency in software engineering design practices. Experience working with GenAI models, transformers and deep-learning techniques. Proven track record of deploying and managing ML models in production environments. Familiarity with common ML/NLP libraries such as PyTorch, Tensorflow, HuggingFace Transformers, and SpaCy. 4+ years of developing production-grade applications in Python. Proficiency in Kubernetes and containers. Familiarity with concepts/libraries such as sklearn, kubeflow, argo, and seldon. Expertise in Python is a must. Experience designing, developing, and testing scalable distributed systems. Experience with ML workflow management, like AirFlow, Sagemaker, etc. Experience guiding other Engineers in the past.

Nice to have:


Familiarity with message broker systems (e.g., Kafka, RabbitMQ). Knowledge of application instrumentation and monitoring practices. Familiarity with the AWS ecosystem. Past projects involving the construction of agentic language chains. Experience in C++, Kotlin, or other similar programming languages.




Please note that visa sponsorship is not available for this position.



Fostering a diverse, welcoming and inclusive environment is important to us. We work hard to make everyone feel comfortable bringing their best, most authentic selves to work every day. We celebrate our talented Relics' different backgrounds and abilities, and recognize the different paths they took to reach us - including nontraditional ones. Their experiences and perspectives inspire us to make our products and company the best they can be. We're looking for people who feel connected to our mission and values, not just candidates who check off all the boxes.



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We believe in empowering all Relics to achieve professional and business success through a flexible workforce model. This model allows us to work in a variety of workplaces that best support our success, including fully office-based, fully remote, or hybrid.

Our hiring process

In compliance with applicable law, all persons hired will be required to verify identity and eligibility to work and to complete employment eligibility verification. Note: Our stewardship of the data of thousands of customers' means that a criminal background check is required to join New Relic.



We will consider qualified applicants with arrest and conviction records based on individual circumstances and in accordance with applicable law including, but not limited to, the San Francisco Fair Chance Ordinance.



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