About the company:
CoreOps.AI is a new age company founded by highly experienced leaders from the technology industry with a vision to be the most compelling technology company that modernizes enterprise core systems and operations.
Website : https://coreops.ai
CoreOps is building the AI operating system for enterprises - accelerating modernization by 50% and cutting costs by 25% through intelligent automation, data orchestration, and legacy transformation.
At CoreOps.AI, we believe in the quiet power of transformation like the dandelion that seeds change wherever it lands.
Inspired by this symbol of resilience and growth, our enterprise AI solutions are designed to take root seamlessly, enrich core operations, and spark innovation across your business.Founded by industry veterans with deep B2B expertise and a track record of scaling global tech businesses, CoreOps.AI brings the power of agentic AI to modernize legacy systems, accelerate digital transformation, and shape the future of intelligent enterprises.
Summary:
We are seeking a talented and motivated AI Engineer to join our team and focus on building cutting-edge Generative AI applications. The ideal candidate will possess a strong background in data science, machine learning, and deep learning, with specific experience in developing and fine-tuning Large Language Models (LLMs) and Small Language Models (SLMs). You should be comfortable managing the full lifecycle of AI projects, from initial design and data handling to deployment and production monitoring. A foundational understanding of software engineering principles is also required to collaborate effectively with engineering teams and ensure robust deployments.
Responsibilities:
- Design, develop, and implement Generative AI solutions, including applications leveraging Retrieval-Augmented Generation (RAG) techniques.
- Fine-tune existing Large Language Models (LLMs) and potentially develop smaller, specialized language models (SLMs) for specific tasks.
- Manage the end-to-end lifecycle of AI model development, including data curation, feature extraction, model training, validation, deployment, and monitoring.
- Research and experiment with state-of-the-art AI/ML/DL techniques to enhance model performance and capabilities.
- Build and maintain scalable production pipelines for AI models.
- Collaborate with data engineering and IT teams to define deployment roadmaps and integrate AI solutions into existing systems.
- Develop AI-powered tools to solve business problems, such as summarization, chatbots, recommendation systems, or code assistance.
- Stay updated with the latest advancements in Generative AI, machine learning, and deep learning.
Qualifications:
- Proven experience as a Data Scientist, Machine Learning Engineer, or AI Engineer with a focus on LLMs and Generative AI.
- Strong experience with Generative AI techniques and frameworks (e.g., RAG, Fine-tuning, Langchain, LlamaIndex, PEFT, LoRA).
- Solid foundation in machine learning (e.g., Regression, Classification, Clustering, XGBoost, SVM) and deep learning (e.g., ANN, LSTM, RNN, CNN) concepts and applications.
- Proficiency in Python and relevant libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch).
- Experience with data science principles, including statistics, hypothesis testing, and A/B testing.
- Experience deploying and managing models in production environments (e.g., using platforms like AWS, Databricks, MLFlow).
- Familiarity with data handling and processing tools (e.g., SQL, Spark/PySpark).
- Basic understanding of software engineering practices, including version control (Git) and containerization (Docker).
- Bachelors or master s degree in computer science, Artificial Intelligence, Data Science, or a related quantitative field.
Preferred Skills:
- Experience building RAG-based chatbots or similar applications.
- Experience developing custom SLMs.
- Experience with MLOps principles and tools (e.g., MLFlow, Airflow).
- Experience migrating ML workflows between cloud platforms.
- Familiarity with vector databases and indexing techniques.
-
Experience with Python web frameworks (e.g., Django, Flask).
-
Experience building and integrating APIs (e.g., RESTful APIs).
-
Basic experience with front-end development or UI building for showcasing AI applications.