Note: The job is a remote job and is open to candidates in USA. Quantiphi is an award-winning Applied AI and Big Data software and services company, driven by a deep desire to solve transformational problems at the heart of businesses. They are seeking a Senior Machine Learning Engineer to lead the design and implementation of advanced AI/ML solutions, develop sophisticated machine learning models, and optimize MLOps pipelines.
Responsibilities
- Lead the architecture, design, and implementation of advanced AI/ML solutions, including generative AI, large language models (LLMs), and agentic systems on GCP
- Develop and deploy sophisticated machine learning models, focusing on deep learning, natural language processing (NLP), computer vision, and reinforcement learning
- Design and optimize end-to-end MLOps pipelines for continuous integration, delivery, and training of AI/ML models, ensuring scalability and reliability on GCP
- Translate complex business challenges into innovative AI/ML technical requirements and architectural blueprints
- Leverage a wide range of GCP AI/ML services (e.g., Vertex AI, BigQuery ML, Cloud AI Platform) to build and manage AI solutions efficiently
- Ensure the security, compliance, and governance of AI/ML systems within the GCP environment
- Collaborate with data scientists, product managers, and engineering teams to integrate AI/ML solutions into core products and services
- Provide technical leadership and mentorship to junior engineers, fostering best practices in AI/ML development and MLOps
- Stay abreast of the latest advancements in AI research, particularly in generative AI, LLMs, and responsible AI, and apply them to practical solutions
- Optimize the performance and cost-efficiency of AI/ML workloads on GCP
Skills
- Expertise in designing and implementing AI solutions, including generative AI, large language models (LLMs), and multi-agent systems
- Strong understanding of deep learning architectures (e.g., Transformers, CNNs, RNNs) and their application in NLP and computer vision
- Proficiency in machine learning algorithms, statistical modeling, and advanced data science techniques
- Demonstrated experience with the full MLOps lifecycle, including model versioning, deployment, monitoring, and retraining
- Expert-level proficiency in Python, including TensorFlow, PyTorch, Hugging Face Transformers, and other relevant AI/ML libraries
- Deep hands-on experience with Google Cloud Platform (GCP) AI/ML services, especially Vertex AI (Training, Prediction, Pipelines, Feature Store, Workbench)
- Strong experience with GCP data services such as BigQuery, Cloud Storage, Dataflow, and Pub/Sub for AI/ML data pipelines
- Familiarity with containerization technologies (Docker) and orchestration tools (Kubernetes/GKE) for deploying AI/ML models
- Solid understanding of software engineering best practices, including clean code, testing, and version control (Git)
- Ability to design and implement robust, scalable, and secure AI/ML architectures on cloud platforms
- Excellent problem-solving skills and the ability to communicate complex technical concepts clearly to diverse audiences
- Experience with prompt engineering, fine-tuning, and deployment of LLMs
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