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