Machine Learning Engineer - Generative AI Focus

Overview We are seeking a highly skilled and innovative ML Engineer with 5-9 years of experience to join our team. The ideal candidate will be instrumental in designing, developing, and deploying advanced machine learning solutions, with a significant focus on Generative AI (GenAI). This role requires hands-on experience primarily with Google Cloud Platform (GCP), leveraging advanced GCP services, and a deep understanding of Responsible AI principles. You will build scalable, high-performance, and ethically sound GenAI applications with production-level workload experience (MLOps etc)

Key Responsibilities

● Design, develop, and implement machine learning models and algorithms to solve complex business problems, with a strong emphasis on Large Language Models (LLMs) and other Generative AI techniques. ● Architect, build, and maintain an LLM apps to manage, route, and optimize access to various LLM providers (e.G., OpenAI, Google's PaLM API, Anthropic Claude) and internal fine-tuned models, ensuring scalability, security, and cost-efficiency. ● Collaborate with data scientists and research teams to transition GenAI models from research and development to production-ready systems, primarily within the Google Cloud Platform ecosystem. ● Build and maintain scalable data pipelines for GenAI model training, fine-tuning, and inference using Vertex AI Pipelines and Dataflow, handling large and diverse datasets. ● Develop and implement strategies for prompt engineering, retrieval-augmented generation (RAG), and fine-tuning of LLMs to achieve desired outcomes. ● Deploy and manage GenAI models in production environments on GCP, leveraging services like Vertex AI, Google Kubernetes Engine (GKE), Cloud Functions, Cloud Run, and Vertex AI Model Garden. ● Implement comprehensive MLOps practices including automated model training pipelines, continuous integration/continuous deployment (CI/CD) for ML models, automated testing, and model governance frameworks. ● Ensure the reliability, performance, and scalability of GenAI systems in production, with a focus on low-latency inference and high throughput for LLM applications. ● Monitor GenAI model performance, detect drift, and implement automated retraining/fine-tuning strategies using Vertex AI Model Monitoring and MLOps orchestration tools. ● Integrate Responsible AI principles throughout the GenAI lifecycle, including fairness, explainability, privacy, and safety considerations for LLMs. ● Establish and maintain production-grade MLOps workflows including model versioning, experiment tracking, automated deployment, rollback strategies, and performance monitoring. ● Document model architecture, data flows, LLM Gateway design, MLOps procedures, and operational runbooks.

Must-Have Skills & Requirements:

Experience

● 5-9 years of professional experience as an ML Engineer or similar role with demonstrated production-level GenAI and MLOps experience ● Bachelor's or Master's Degree in Computer Science, Engineering, Statistics, or a related quantitative field

Core Technical Skills

● Strong proficiency in Python programming for data manipulation, machine learning, and scripting ● Hands-on experience with machine learning frameworks such as Scikit-learn, TensorFlow, PyTorch, or Keras ● Experience with building and managing APIs for machine learning models, particularly for LLMs (e.G., FastAPI, Flask)

Google Cloud Platform Expertise: ● Proven experience working with Google Cloud Platform services, specifically: Vertex AI (including Generative AI Studio, Model Garden, Pipelines) Google Kubernetes Engine (GKE) for container orchestration Cloud Functions and Cloud Run for serverless deployments Dataflow for stream and batch data processing BigQuery for data warehousing and analytics Cloud Storage for data lake management Vertex AI Workbench for ML development GenAI & Responsible AI: ● Familiarity with LLM specific frameworks and libraries (e.G., Hugging Face Transformers, LangChain, LlamaIndex) ● Understanding of and practical experience with Responsible AI principles (fairness, explainability, privacy, safety, and transparency), especially in the context of LLMs ● Ability to work with large datasets and distributed computing frameworks Soft Skills: ● Strong problem-solving skills and attention to detail ● Excellent communication and collaboration skills