Senior Machine learning Engineer

Your opportunity

Join our dynamic team as a Senior Machine Learning Engineer, where you'll drive innovation across the entire ML lifecycle. You'll work on cutting-edge conversational AI bots, a Retrieval-Augmented Generation (RAG) system, and traditional ML solutions for our observability platform. This role involves both operational and engineering tasks, including building production-ready inference pipelines, deploying and versioning models, and implementing continuous validation processes. You will also focus on fine-tuning generative AI models, designing agentic language chains, and prototyping recommender system experiments.

What you'll do

• Model Fine-Tuning: Enhance the performance of generative AI models.
• AI Agent Design: Create intelligent agents for conversational AI applications.
• Innovation & Experimentation: Utilize new techniques to develop models for observability-related use cases.
• Pipeline Development: Build and maintain efficient inference pipelines for model deployment.
• Model Management: Oversee deployment and model versioning for seamless updates.
• Continuous Validation: Develop tools to ensure model accuracy and performance in production environments.

This role requires

Experience: 5+ years in machine learning or related fields.
Education: Bachelor’s or advanced degree (Master's or Ph.D.) in Computer Science, Engineering, Mathematics, or related disciplines.

Technical Expertise:

• Proven proficiency in deploying and managing ML models in production.
• Hands-on experience with transformer models and text embeddings.
• Familiarity with ML/NLP libraries, such as PyTorch, TensorFlow, Hugging Face Transformers, and SpaCy.
• 3+ years of experience with Python for developing production-grade applications.
• Proficiency in Kubernetes and containerization technologies.
• Knowledge of scikit-learn, Kubeflow and Seldon.
• Expertise in Python, C++, Kotlin, or similar programming languages.
• Experience creating scalable distributed systems.
• Familiarity with message broker systems (e.g., Kafka, RabbitMQ).
• Understanding of application instrumentation and monitoring practices.
• Experience with ML workflow management tools like Airflow, Argo or SageMaker.
  

Bonus points if you have

• Familiarity with the AWS ecosystem.
• Experience with the construction of agentic language chains.

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