Machine Learning Engineer Data Science

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.