Architect - GenAI LLM/ML

Description

Organization is seeking a highly skilled and experienced AI/ML Architect to design, develop, and deploy impactful AI/ML solutions across the organization. This role will be responsible for architecting and implementing scalable AI/ML platforms, driving the development of core ML use cases such as demand forecasting, supply chain optimization, and predictive maintenance, as well as contributing to computer vision and natural language processing applications. The ideal candidate will possess deep expertise in machine learning algorithms, a proven track record of deploying production-grade AI/ML solutions, and a strong understanding of MLOps principles and Explainable AI (XAI) techniques. This individual will be a hands-on technical leader, capable of both strategic thinking and technical execution.

Primary responsibilities:

Solution Architecture & Design:

- Design and architect scalable, reliable, and secure AI/ML platforms and solutions.

- Define the technical specifications for AI/ML applications, including data pipelines, feature engineering, model training, deployment, and monitoring.

- Lead the selection and evaluation of appropriate AI/ML tools, frameworks, and cloud services.

- Develop and maintain architecture patterns and guidelines for AI/ML development.

- Ensure compliance with industry standards and regulations.

Core ML Use Case Implementation :

- Lead the development and implementation of core ML use cases, including but not limited to:

- Demand Forecasting: Developing models to predict future demand for Organization products and services.

- Supply Chain Optimization: Optimizing inventory levels, logistics, and distribution networks using AI/ML.

- Predictive Maintenance: Building models to predict equipment failures and schedule maintenance proactively.

- Collaborate with business stakeholders to understand requirements and translate them into technical solutions.

- Develop and implement data pipelines for collecting, cleaning, and preparing data for model training.

- Evaluate and select appropriate machine learning algorithms for each use case.

- Train, validate, and deploy machine learning models.

- Monitor model performance and retrain models as needed.

Computer Vision & NLP:

- Contribute to the development of computer vision applications, such as image recognition, object detection, and video analytics.

- Contribute to the development of natural language processing applications, such as text classification, sentiment analysis, and chatbot development.

- Stay abreast of the latest advancements in computer vision and NLP technologies.

MLOps & Deployment:

- Design and implement MLOps pipelines for automating the deployment, monitoring, and management of AI/ML models.

- Define infrastructure requirements for running AI/ML models at scale.

- Implement monitoring and alerting systems to ensure the reliability and performance of AI/ML deployments.

- Develop strategies for managing model versions and ensuring reproducibility.

- Collaborate with DevOps teams to automate the deployment and scaling of AI/ML infrastructure.

Explainable AI (XAI):

- Implement XAI techniques to understand and explain the decisions made by AI/ML models.

- Develop methods for visualizing and interpreting model results.

- Ensure that AI/ML models are transparent and explainable to stakeholders.

- Address ethical considerations related to AI/ML model bias and fairness.

Production-Grade AI Solutions:

- Lead the development and deployment of production-grade AI/ML solutions, ensuring scalability, reliability, and security.

- Implement best practices for AI/ML model monitoring, retraining, and governance.

- Work closely with data engineers, data scientists, and software engineers to deliver end-to-end AI/ML solutions.

- Ensure compliance with security and regulatory requirements.

- Optimize AI/ML models for performance and cost efficiency.

Technical Leadership & Mentorship:

- Provide technical leadership and mentorship to AI/ML engineers and data scientists.

- Stay abreast of the latest advancements in AI/ML technologies.

- Present technical findings and recommendations to senior management.

- Promote a culture of innovation and continuous learning within the AI/ML team.

Collaboration & Communication:

- Work closely with business stakeholders, product managers, and engineering teams to define requirements and deliver solutions.

- Effectively communicate technical concepts to both technical and non-technical audiences.

- Participate in industry conferences and events to share knowledge and network with peers.

- Build strong relationships with vendors and partners in the AI/ML ecosystem.

Data Governance and Security:

- Ensure that all AI/ML solutions comply with Organization's data governance and security policies.

- Implement appropriate security measures to protect sensitive data.

- Work closely with the security team to identify and mitigate potential risks.

Experience and Skills Required:

- Education: Bachelor's degree in Computer Science or Electronics and communication or a related field. Master's or Ph.D. preferred.

Experience:

- Overall 14 years and minimum 8 years of experience in AI/ML, with a focus on building and deploying production-grade solutions.

- Proven experience in implementing core ML use cases such as demand forecasting, supply chain optimization, and predictive maintenance.

- Experience with computer vision and natural language processing applications.

- Experience with MLOps principles and tools.

- Experience with Explainable AI (XAI) techniques.

Technical Skills:

- Deep understanding of AI/ML algorithms and techniques, including deep learning, natural language processing, and computer vision.

- Proficiency in programming languages such as Python, Java, or R.

- Experience with AI/ML frameworks such as TensorFlow, PyTorch, scikit-learn, and XGBoost.

- Experience with cloud platforms such as AWS, Azure, or Google Cloud.

- Experience with data engineering tools such as Apache Spark, Hadoop, and Kafka.

- Strong understanding of data structures, algorithms, and software design principles.

- Experience with containerization technologies such as Docker and Kubernetes.

- Familiarity with DevOps practices and tools.

- Experience with databases such as SQL and NoSQL.

Soft Skills:

- Strong leadership and communication skills.

- Ability to work independently and as part of a team.

- Excellent problem-solving skills.

- Ability to manage multiple projects simultaneously.

- Strong attention to detail.

- Ability to learn new technologies quickly.

Key Competencies:

- Technical Expertise: Demonstrates deep knowledge of AI/ML and related technologies.

- Strategic Thinking: Able to develop and articulate a clear and compelling AI/ML strategy.

- Problem Solving: Identifies and solves complex technical problems.

- Leadership: Provides technical leadership and mentorship to others.

- Communication: Communicates effectively with both technical and non-technical audiences.

- Collaboration: Works effectively with others to achieve common goals.

- Innovation: Generates new and innovative ideas.

- Results Orientation: Delivers high-quality results on time and within budget.