Opening for Sr/ Architect Gen AI

2 weeks ago

Bengaluru, India Quantiphi Full time

About the Role:

We are seeking a highly skilled and experienced Machine Learning Systems Architect with a strong focus on Generative AI to join our growing team. As a Machine Learning Systems Architect, you will play a pivotal role in architecting and building production-grade Generative AI solutions that drive innovation and deliver tangible business value. You will be responsible for leading the design, development, and deployment of complex AI/ML systems, ensuring scalability, reliability, and performance.


Responsibilities:


Technical Leadership: Lead the technical design and architecture of complex Generative AI systems, ensuring alignment with business requirements and technical feasibility.


Solution Architecture: Design and develop robust and scalable Generative AI solutions, including data pipelines, model training, deployment, and monitoring using MLOps tools (Kubeflow, MLflow, TFX).


Production Deployment: Lead the deployment of Generative AI models into production environments, leveraging cloud platforms and open-source tools like Vertex AI, SageMaker, and open-source frameworks.


Model Optimization: Identify and implement optimization strategies such as hyperparameter tuning, feature engineering, and model selection for enhanced performance.


Team Collaboration: Collaborate effectively with cross-functional teams, including data scientists, engineers, product managers, and stakeholders, to ensure successful project delivery.


Technical Expertise: Stay abreast of the latest advancements in Generative AI technologies and methodologies, and contribute to the development of best practices within the organization.


Mentorship: Mentor junior team members, fostering their growth and development in the field of Generative AI.








Qualifications:


Must-Have Skills:

Experience:

10+ years in software development, with 8+ years in AI/ML applications.

Proven track record of solving complex business problems using AI/ML.

Expertise in data exploration, feature engineering, and hypothesis testing.

Excellent communication skills to convey technical concepts to diverse audiences.

Proven experience working in an agile, collaborative environment.

Cloud & AI Tools: Expertise in GRPC, MLflow, Kubeflow, TFX, and cloud AI platforms like AWS, GCP, and Azure, with hands-on experience in AI APIs (Speech-to-text, Vision, Text Extraction).

Machine Learning:

Algorithms: Regression, Classification (Random Forests, SVM, ANNs), Clustering (k-means, DBSCAN), Dimensionality Reduction (PCA), and Ensembles (XGBoost, LightGBM).

NLP Techniques: Traditional (Bag of Words, tf-idf, Stemming, Stemming, Lemmatization, Tokenization, POS tagging, Coreference Resolution, Named Entity Recognition, etc) and advanced techniques (Transformers, GPT, BERT, RNNs, LSTMs, Sequence modeling, Attention mechanism, etc ), Text classification, Sentiment Analysis, Semantic similarity, Entity Extraction, Document Summarization, NLI, Question-Answering, Machine Translation using Transformers, BERT, GPT, etc.

Deep Learning: Proficiency in CNNs, RCNNs, YOLO, and frameworks like TensorFlow, PyTorch, and Keras.

Forecasting Models: Expertise in Linear Regression, Neural Networks, ARIMA, LSTM, and Exponential Smoothing for time-series predictions.

Generative AI:

Text/Audio/Image Generation: Experience with GPT-3, BERT, Transformers, GPT, DALL-E, Stable Diffusion, WaveNet, and GANs.

Frameworks: Proficiency in open-source Generative AI tools and a deep understanding of ethical considerations in AI models.

Development Tools:

Programming: Python/R with libraries like Scikit-Learn, Pandas, and Matplotlib.

APIs: Hands-on with Flask, Django, and FastAPI for API development.

Visualization: Experience with Tableau, Power BI, and Looker.


Nice-to-Have:

Pre-sales experience

Hands-on experience with visualization tools like Tableau, Power BI, Looker Studio.

Advanced image processing using OpenCV, feature detection using SIFT, SURF, FAST, etc.

Experience with recommender systems using techniques like Matrix Factorization, KNN, SVD, and Deep Learning.

Expertise in distributed training using TensorFlow and PyTorch.

Proficiency with Docker containerization and orchestration with Kubernetes.

Experience with Knowledge Graphs, Federated Learning, and Deep Reinforcement Learning.

Expertise in Intelligent Document Processing OCR technologies (e.g., Tesseract, OpenCV), cloud-based document processing tools like Google Document AI, Amazon Textract, Azure Form Recognizer.

Cloud Certifications like Machine Learning and/or Cloud Architect.

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