Senior Machine learning Engineer

Job Description

Your opportunity Join our dynamic team as a Senior Machine Learning Engineer, where youll drive innovation across the entire ML lifecycle. Youll 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 youll 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 (Masters 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