Job Overview
We are seeking an AI-ML Tech Engineer with a strong background in machine learning, data science, and software engineering. The ideal candidate will have experience in developing and deploying machine learning models, handling large datasets, and collaborating with cross-functional teams to solve real-world challenges.
Key Responsibilities
- Develop and deploy machine learning and NLP solutions using common ML libraries and frameworks.
- Work with large datasets and distributed computing systems to extract meaningful insights.
- Fine-tune deep learning models, including large language models (LLMs) and small language models (SLMs).
- Implement and optimize ML pipelines for training, evaluation, and inference.
- Utilize AI cloud services for efficient model deployment and management.
- Develop and maintain CI/CD pipelines for ML training and deployment.
- Work on AI-powered solutions involving Vector Stores and Retrieval-Augmented Generation (RAG) pipelines.
- Translate ML-based outcomes into business-digestible insights.
- Collaborate with stakeholders to define project requirements and deliver innovative AI solutions.
Mandatory Skills & Qualifications
- Expertise in Python and ML frameworks (TensorFlow, PyTorch, Keras, Scikit-learn).
- Strong understanding of statistical models, regression, clustering, and ML algorithms (e.g., decision trees, Random Forests, neural networks).
- Experience in deploying ML models in production environments.
- Proficiency in cloud AI services (AWS, Azure, or GCP).
- Knowledge of large language models like OpenAI's GPT-3.5, GPT-4, and Codex.
- Hands-on experience with vector databases and RAG-based ML solutions.
- Proficient in various ML deployment strategies, both static and dynamic.
- Experience in MLOps, automation, and CI/CD pipelines for ML workflows.
- Excellent communication skills and experience managing stakeholders.
Nice-to-Have Skills
- Experience with MLOps tools for continuous integration and deployment.
- Knowledge of full-stack development and API frameworks (Flask, Django).
- Familiarity with Databricks, Data Mesh, and ETL pipelines.
- Hands-on experience with Docker containers.
- Exposure to agile methodologies such as Scrum or Kanban, and project management tools like JIRA/GitLab.
