AI ML Engineer

• Experience in Part of Speech Tagging - Use several techniques, including table lookups, n-grams, and hidden Markov models, to tag parts of speech in sentences, and compare their performance.
• Machine Translation Able to build a deep neural network that functions as part of an end-to-end machine translation pipeline. The completed pipeline will accept English text as input and return the French translation.
• Speech Recognizer Able to build a deep neural network that functions as part of an end-to-end automatic speech recognition (ASR pipeline. The model will convert raw audio into feature representations, which will then turn them into transcribed text.
• Experience in Image Representation & Analysis
• Able to understand how images are represented numerically.
• Implement image processing techniques like color and geometric transforms.
• Program ones own convolutional kernel for object edge-detection.
• Experience in Convolutional NN Layers - Know region-based CNNs, like Faster R-CNN, have allowed for fast, localized object recognition in images. Knowledge in YOLO/single shot object detection system.
• Features & Object Recognition Able to extract information about an objects color and shape. Use features to identify areas on a face and to recognize the shape of a car or pedestrian on a road.
• Image Segmentation - Implement k-means clustering to break an image up into parts. Find the contours and edges of multiple objects in an image.
• Knowledge to combine CNNs and RNNs to build a complex captioning model. Implement an LSTM for caption generation. Ability to train a model to predict captions and understand a visual scene.
• Architect and execute model deployment strategies, harnessing containerization (Docker) and orchestration (Kubernetes) for exceptional scalability and reliability.
• Engineer automated CI/CD pipelines that facilitate seamless model deployment, monitoring, and continuous optimization.
• Define and refine performance benchmarks, and optimize models and infrastructure to achieve peak efficiency, scalability, and robustness.
• Remain at the forefront of industry trends and emerging technologies, expertly integrating the latest advancements into our Client ecosystem.

Requirements:

• 7+ years of experience with a significant focus on developing, deploying & supporting AI solutions in production environments.
• Proven experience in building enterprise software products for B2B businesses
• Solid engineering and coding skills and understanding of data structures, algorithms, and software design principles. Ability to write high performance production quality code in Python
• Proficiency with Client libraries, platforms & frameworks (eg, PyTorch, scikit-learn).
• Strong expertise in using stat, Client, DL and foundation models
• Strong understanding of AI and machine learning algorithms.
• Experience in deploying AI models in cloud or on-prem environments.
• Experience with MLOps tools (MLFlow) and practices for continuous integration and deployment of Client models using Kubeflow and Kserve.
• Has familiarity with deploying applications on Kubernetes
• Exposure to generative AI models and agent architectures on platforms such as AWS or Azure
• Good to have experience in the GenAI technologies like Prompt Engineering, Azure openAI models like GPT3.5, GPT4, VectorDB, langchain, etc
• Excellent problem-solving skills and the ability to work independently as we'll as in a collaborative team environment.