Jobdesc:
- Develop and implement machine learning models and algorithms to solve business problems, leveraging both structured and unstructured data.
- Design and build scalable and efficient data pipelines for data ingestion, preprocessing, and feature engineering.
- Collaborate with data scientists to understand business requirements and translate them into technical specifications.
- Conduct exploratory data analysis to gain insights and identify patterns or trends.
- Evaluate and select appropriate machine learning techniques, models, and algorithms based on the project requirements.
- Train, optimize, and fine-tune machine learning models using large-scale datasets.
- Validate and test models for accuracy, reliability, and robustness.
- Monitor and maintain deployed models, ensuring their performance and stability.
- Collaborate with Data Engineers to integrate machine learning models into production systems.
- Stay up to date with the latest advancements in machine learning and related fields, and actively contribute to the team's knowledge base.
- Communicate findings, results, and recommendations to both technical and non-technical to internal stakeholders or customers.
Requirements:
- At least 1 year of experience in Machine Learning & Data Science
- Bachelor's degree in Computer Science, Data Science, Mathematics, Statistics, or a related field. A master's or Ph.D. degree is a plus.
- Proven experience as a Machine Learning Engineer or similar role. A strong track record of developing and deploying machine learning models in real-world applications is essential.
- Experience with Generative AI by leveraging LLM model like Gemini, GPT 4o, Claude, Mistral, etc.
- Solid understanding of RAG technique in LLM model, Fine tuning, Prompt Engineering.
- Proficiency in programming languages such as Python, R, or Scala, with experience in popular machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Solid understanding of machine learning techniques, including supervised and unsupervised learning, deep learning, natural language processing, and/or reinforcement learning.
- Experience with data manipulation, preprocessing, and feature engineering techniques.
- Strong knowledge of statistical analysis, hypothesis testing, and experimental design.
- Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and distributed computing frameworks (e.g., Hadoop, Spark).
- Proficiency in SQL and database management systems.
- Strong problem-solving skills and the ability to work on complex and ambiguous business problems.
- Excellent communication and teamwork skills, including the ability to collaborate effectively with cross-functional teams.