Job Summary:
We are seeking a highly skilled and experienced AI/ML Engineer to join our growing AI team. The ideal candidate will have a strong foundation in machine learning, deep learning, and data science, with hands-on experience in building scalable AI solutions using open-source tools and cloud infrastructure. You will work on cutting-edge projects involving generative AI, predictive modeling, and intelligent automation.
Key Responsibilities:
- Design, build, and deploy advanced ML models for applications such as forecasting, anomaly detection, clustering, trend analysis, and pattern recognition.
- Develop and optimize GenAI solutions leveraging models like GPT-3.5/4/5, LLaMA 2, Falcon, Gemini and apply prompt engineering best practices.
- Build and maintain basic Retrieval-Augmented Generation (RAG) pipelines.
- Process and analyze both structured and unstructured data from diverse sources.
- Implement, test, and deploy ML models using FastAPI, Flask, Docker, and similar frameworks.
- Conduct data preprocessing, feature engineering, and statistical analysis to prepare datasets for modeling.
- Collaborate with cross-functional teams to integrate models into production systems hosted on AWS (EC2, S3, ECR).
- Evaluate model performance using standard metrics and iterate on improvements.
Required Skills and Experience:
- 4+ years of hands-on experience in AI/ML and Data Science with a strong grasp of open-source ML/DL tools.
- Proficient in Python and data science libraries such as NumPy, SciPy, Scikit-learn, Matplotlib, and CUDA for GPU computing.
- Strong experience in at least one of the following:
- Time Series Analysis
- Standard Machine Learning Algorithms
- Deep Learning Architectures
- Hands-on experience with GenAI models and prompt engineering techniques.
- Working knowledge of cloud platforms, preferably AWS.
- Familiarity with containerization and model deployment (Docker, FastAPI, Flask).
- Solid understanding of statistics, model validation techniques, and evaluation metrics.
Preferred Expertise (One or More):
- Proficiency with object detection frameworks such as YOLO, Detectron, TFOD, ideally in a distributed computing environment.
- Deep knowledge of deep learning architectures like CNN, RNN, Transformers (LSTM, ResNet, etc.).
- Experience with NLP models and frameworks, including BERT, ELMo, GPT-2, XLNet, T5, CRFs, and ONNX.
Additional Qualities:
- Strong analytical and problem-solving skills.
- Ability to communicate technical concepts effectively.
- Proactive decision-making and a strong sense of ownership.
