Lead AI/ML Architect

Excellent Opportunity with Global MNC :

Masters degree in AI/ML with 5+ years hands-on experience in DS, AI, ML, DL, NLP, RL; expertise in Python, R, TensorFlow, PyTorch, AWS/GCP platforms.

Lead AI/ML strategy, build CoE and teams, develop and deploy machine learning models for Electronic Warfare and SIGINT domains, manage large-scale AI projects, and collaborate with domain experts.

Experience Requirements:

- Good 5+ years of relevant hands on experience in AI/ML Architecture, Machine Learning Solutions, Customer-Facing AI Applications, Conversational AI, Predictive Analytics, Operational Intelligence, Data Science, MLOps, NVIDIA Deep Learning, AWS Machine Learning, Microsoft Azure AI/ML, Google Cloud AI/ML, CAIP.

- Experience working in range of problems involving various areas of AI ML.

- Hands on experience developing models using ML, DL, ADL, NLP and RL strategy.

- Leading innovation effort.

- Experience in Python, R, AI Framework, TensorFlow, PyTorch.

- Experience building AI ML pipeline.

- Experience handling large dataset, quantitative and statistical analysis.

- Good knowledge of statistics.

- Implementation knowledge of various algorithms, optimizers.

- Experience with Natural Language models and approaches including Topic Modeling, Text Classification, Entity Extraction, Neural Language (CNN, BiDirectional LSTM, Elmo, Bert).

- Deep understanding of text representation techniques (n-grams, bag of words, tf-idf, word embeddings, sense embeddings) and frameworks (glove, word2vec).

- Experience managing large scale AI ML projects using AWS / Google platform.

- Auto ML.

- Publication.

- Experience in AI frameworks, such as PyTorch, Apply machine learning models, tools, and techniques to the Electronic Warfare and SIGINT domains.

- Assist with algorithm and model integration with hardware and software components.

- Assist with the collection, generation, and augmentation of data sets.

- Perform verification and validation of system functionality and performance.

- Work with domain subject matter experts to identify algorithm and model requirements.

- Stay updated on state-of-the-art ML techniques and technologies.

- Experience with some of: Temporal Data Streams, Digital Signal Processing, Wireless Communications, Intelligent Agents, Artificial Intelligence (AI), Deep Reinforcement Learning, Neural Networks, Python, Matlab, Tensorflow, Keras, Kubeflow, Sagemaker.

- Creating, designing, developing, testing, calibrating, deploying, and supporting both new and existing models and solutions.

- Provide technical and management leadership in the application of data science methods, including using data mining, machine learning, artificial intelligence, and big data concepts.

- Research, develop AI/DL/ML algorithms to address exciting and difficult AI tasks.

- Assist with architecture and design data approaches and solutions for a large set of data.

- Manage large & complex analytical projects: data exploration, model building, performance evaluation & testing.

- Expert experience with cloud providers (AWS, Azure) and their machine learning tools.

- Expert experience with data pipelines, data tools, and data organization for analytics.

- Expert experience with building machine learning or other types of models.

- Comprehensive knowledge of math, probability, statistics and algorithms.

- Statistical knowledge and proven competence.

- Comprehensive understanding of data structures, data modeling and software architecture.

- Hands-on experience with machine learning frameworks (like tensorflow or PyTorch) and libraries (like scikit-learn) across all aspects of model development.

- Deep experience with AWS SageMaker, Azure ML, or other ML development platforms.

- Deep experience with programming languages such as Python, R; and query languages such as SQL.

- Execute AI governance operations including assessments, risk tracking, controls monitoring, and L1 support.

- Maintain a strategic roadmap for AI governance roll-out across the enterprise.

- Experience in machine learning, deep learning, and computer vision (e.g. CNNs, Detection Networks, Segmentation Networks, etc.).

- Knowledge of data science technologies (AWS/C2S specifically Sagemaker, Docker, Kubernetes, Keras, Tensorflow, etc.).

- Develop reference architectures, samples and other materials to share with the broader PyTorch developer community.

- 2+ years of experience in one or more of the following areas: Deep Learning, Computer Vision, NLP, Speech, Conversational AI, Dialogue, Robotics, AI-Infrastructure, Machine Learning or artificial intelligence.

- Experience with at least one Deep Learning framework such as PyTorch, TensorFlow, MXNet, Caffe.

- Design, develop and implement analytical solutions using a variety of commercial and open-source tools (common tools include Python, Keras, or TensorFlow).

- Experience in building deep learning models, preferably with exposure to functional genomics, molecular and cellular biology, or modeling dynamic systems.

- Experience with at least one Deep Learning frameworks such as TensorFlow, Keras, or PyTorch.

- Be a hands-on leader and teach by example by building prototypes using variety of predictive methods while adhering to the ML development cycle (e.g. iterative EDA, prediction specification, feature engineering and model tuning).

- Experience with one or many of these topics such in customer churn, intent prediction, disease/damage progression, anomaly detection, sequence-based models, time-series forecasting or time to event modeling.

- Exposure to extracting and manipulating data via tool/libraries/forms such as Spark, pandas, scikit family, numpy/scipy, matplotlib, streamlit.

- Extensive experience across the python data science stack - NumPy, Pandas, Scikit-Learn, Pytorch, TensorFlow/Keras, SciPy, Matplotlib.

- Industry experience with multiple of the following: NLP, Deep Learning, traditional supervised and unsupervised learning methods.

- Builds complex programs for running mathematical or statistical tests on data and for understanding complex relationships across attributes.

- Experience formulating, approaching, and solving complex analytical problems using a quantitative, scientific approach.

- Experience working with large, complex datasets using big data technologies and script.

- Experience managing data to scale using data summarization, query, and analysis software and tools.

(ref:hirist.tech)