Engage with clients to understand their business requirements and provide expert advice on leveraging ML and AI technologies to solve their problems
Design end-to-end ML solutions that address client needs, considering factors such as data acquisition, preprocessing, feature engineering, model selection, and deployment
Architect scalable and reliable ML systems that can handle large volumes of data and real-time processing. Ensure the solutions are robust, secure, and scalable, taking into account performance, latency, and cost optimization
Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts to deliver successful ML projects
Stay up-to-date with the latest advancements in ML, AI, and related technologies, identifying opportunities for innovation and differentiation
Conduct research and experimentation to explore new ML algorithms, techniques, and frameworks that can enhance the company's offerings
Present findings and results to internal teams and external stakeholders in a clear and concise manner
Requirements
3+ years of experience as an ML Engineer or Data Scientist, either in academia or industry
Proficient in Python programming and experience with Python data science frameworks. Strong programming skills with proven experience in implementing Python-based machine learning solutions
Familiarity with common ML frameworks (e.g., PyTorch, Keras) and libraries (e.g., NumPy, scikit-learn)
Experience with LLM agents including tool using and reasoning, for instance, the combination of RAG solution and code interpreter
Experience with LLM fine tuning
Solid knowledge of machine learning and deep learning fundamentals
Experience with transformer-based language models
Ability to interpret and implement research ideas and algorithms
Hands-on experience with relational SQL and NoSQL databases
Upper-Intermediate or higher level of English proficiency
Ability to work with external clients and strong communication skills, including presenting in webinars and conferences
Ability to mentor team members and assist in their professional development
Quick learner with the ability to adapt to new technologies, frameworks, and algorithms
Nice to have
Experience with designing complex multi-model and multi-modal ML applications and products
Solid foundation in development of data analytics systems, including data exploration/crawling, feature engineering, model building, performance evaluation, and online deployment of models
Experience with cloud-based tools and technologies for data pipelining, model development, and deployment, particularly AWS (Amazon Web Services)
Familiarity with AI/ML operational tools such as Airflow, MLFlow, H2O, etc.
Experience with MLOps tools and frameworks like Jupyter Notebook, Kubernetes, Kubeflow, Spark, etc.
Experience in building scalable AI/ML systems for continuous training automation, computer vision, natural language processing, or similar advanced AI/ML problems
Engineering experience in model tuning using CUDA/OpenCV, C++, low-level Python scripts, etc.
Up-to-date publications in the areas of deep learning, distributed computing, bioinformatics, or other life sciences
Domain knowledge of biology and/or chemistry
We offer
Competitive compensation
Remote or office work
Flexible working hours
Healthcare benefits: medical insurance and paid sick leave
Continuous education, mentoring, and professional development programs
