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Bengaluru, Karnataka, India
Onsite
Available
Key Responsibilities:
Architectural Design and Strategy:
Develop and maintain the foundational architecture for generative AI initiatives, ensuring scalability, security, and efficiency.
Define the technical roadmap for generative AI capabilities, aligning with business objectives and CDO technology strategies.
Collaborate with stakeholders to gather requirements, provide technical guidance, and deliver AI solutions that meet business needs.
Define, deploy, and advance processes for: AI/ML development, MLOps, LLMOPs, and the monitoring, observability, security, and maintainability of AI models.
Infrastructure and Deployment:
Design and implement the infrastructure required to support generative AI models, including cloud-based solutions, data pipelines, and data storage platforms.
Ensure seamless integration of AI models into production environments, enabling real-time and batch processing capabilities.
Leadership and Best Practices:
Mentor a team of engineers and data scientists in the development and deployment of generative AI models.
Stay abreast of the latest advancements in generative AI and related technologies, incorporating best practices and innovative approaches into the architecture.
Drive continuous improvement of the AI architecture, ensuring it evolves to meet changing business and technological demands.
Governance and Security:
Implement and enforce AI governance standards and security measures to protect sensitive data and ensure regulatory compliance.
Monitor and mitigate risks associated with the deployment and operation of AI models.
Required Qualifications:
Education:
Bachelor’s degree in Computer Science, Engineering, or a related field. Master’s or PhD is a plus.
Experience:
4+ years of experience in designing and implementing AI architectures, with a focus on generative models (e.g., GPT, VAEs, GANs).
10+ years of experience with cloud platforms (AWS preferred) and AI/ML frameworks (TensorFlow, PyTorch).
Experience with Generative AI RAG pattern and LLM fine tuning.
Experience with ML and Generative AI tools such as Amazon SageMaker and Amazon Bedrock.
Knowledge of ethical principles in AI development and deployment, including fairness, accountability, and transparency, ability to identify and address biases in data and models and understanding of techniques for interpreting and explaining AI models.
Skills:
Proficiency in programming languages such as Python, R, Java, and C++ or related modern programming languages.
Deep understanding of machine learning algorithms, natural language processing, and deep learning techniques.
In-depth knowledge of CNNs, RNNs, LSTMs, Transformers, and their applications.
Strong understanding of NLP techniques, including text preprocessing, tokenization, stemming, lemmatization, sentiment analysis, and named entity recognition.
Excellent problem-solving skills and ability to work in a fast-paced, dynamic environment.
Strong communication and collaboration skills, with the ability to effectively convey complex technical concepts to non-technical stakeholders.
Preferred Qualifications:
Experience with MLOps and CI/CD pipelines for AI model deployment.
Experience with AWS services such as SageMaker, Bedrock, EMR, S3, OpenSearch Service, Step Functions, Lambda, and EC2
Familiarity with ethical AI practices and AI governance frameworks.
Experience in the healthcare industry or with healthcare data is desirable.
In depth understanding of healthcare regulations (HIPAA, GDPR, etc.), data privacy, and patient safety standards.
Relocation Assistance Provided: No