Key Responsibilities
- Lead the entire ML lifecycle from data collection and analysis to model deployment, monitoring, and optimization.
- Apply deep learning and NLP techniques to develop solutions, potentially enhancing systems like search or recommendation engines.
- Design and implement end-to-end ML pipelines, incorporating MLOps best practices for CI/CD, containerization (Docker, Kubernetes), and cloud deployment (AWS, GCP, Azure).
- Utilize LLM knowledge, including prompt engineering and fine-tuning, to build advanced generative AI applications and conversational AI solutions.
- Perform comprehensive data analytics, including statistical analysis and feature engineering, to inform model development and extract actionable insights from large datasets.
- Write production-quality, robust code in Python (and potentially other languages like Java or Scala), ensuring code quality through reviews and testing.
- Collaborate with cross-functional teams, including data scientists, data engineers, and product managers, to translate business requirements into technical ML solutions.
Requirements:
Required Skills and Qualifications
- Proven experience as a Machine Learning Engineer with a strong portfolio of deployed production models.
- Proficiency in Python and relevant ML frameworks/libraries (e.g., TensorFlow, PyTorch, scikit-learn).
- Expertise in data science methodologies, statistical analysis, and data analytics.
- Hands-on experience with MLOps tools and practices for managing the ML application lifecycle.
- Strong understanding of NLP and experience with LLMs and prompt engineering techniques.
- Solid software engineering background with knowledge of data structures, algorithms, and system design.
- Excellent problem-solving, communication, and collaboration skills.
Tags & focus areas
Used for matching and alerts on DevFound Ai Machine Learning Deep Learning Data Science Nlp Mlops Generative Ai