AI ML Professional

The AI Lead Developer is responsible for architecting designing and developing advanced AI ML solutions that power intelligent applications and data driven capabilities This role leads the end to end lifecycle of AI development including research model design training deployment monitoring and optimization while guiding a team of engineers and collaborating closely with product data and business stakeholders The AI Lead Developer ensures scalable ethical and production grade AI implementations
Key

• AI ML Architecture Development
• Architect design and develop machine learning and deep learning models for various use cases NLP CV LLMs recommendation systems predictive analytics etc
• Implement end to end AI pipelines including data processing feature engineering model training evaluation and deployment
• Build scalable and efficient model inference systems using microservices APIs or distributed frameworks
• Optimize models for performance accuracy fairness and cost efficiency
• Technical Leadership
• Lead a team of AI ML engineers and data scientists oversee technical direction and code quality
• Provide mentorship conduct code reviews and establish best practices for AI development
• Collaborate with architects and tech leadership to define AI strategy standards and roadmaps
• MLOps Deployment
• Implement ML operationalization pipelines including model versioning CI CD for ML automated retraining and monitoring
• Work with DevOps and Cloud teams to deploy models to AWS Azure or GCP using tools like SageMaker Vertex AI or Azure ML
• Ensure robust monitoring of model drift data drift performance metrics and reliability
• Research Innovation
• Evaluate and incorporate emerging AI technologies transformers LLMs Diffusion models vector databases RAG etc
• Conduct experimentation and POCs to determine feasibility of new AI solutions
• Stay updated with cutting edge AI research frameworks and best practices
• Collaboration Stakeholder Engagement
• Work closely with product managers data teams and business stakeholders to define AI requirements and translate them into technical solutions
• Communicate complex AI concepts in clear actionable terms for non technical audiences
• Drive AI adoption and provide thought leadership across the organization