Position Overview
- Lead Development of Generative AI Applications: Architect and develop advanced generative AI solutions that support business objectives, ensuring high performance and scalability.
- Performance Tuning & Optimization: Identify bottlenecks in applications and implement strategies to improve performance. Optimize machine learning models for efficiency in production environments.
- Collaborate Cross-Functionally: Work closely with data scientists, product managers, and other stakeholders to gather requirements and transform them into robust technical solutions.
- Mentor Junior Engineers: Provide guidance and mentorship to team members on best practices in coding standards, architectural design, and machine learning techniques.
- Research & Innovation: Stay abreast of the latest advancements in the field of Artificial Intelligence. Propose new ideas that could lead to innovations within the organization.
- Deployment & Scaling Strategies: Lead the deployment process of applications on cloud platforms while ensuring they are scalable to handle increasing loads without compromising performance.
- Documentation & Quality Assurance: Develop comprehensive documentation for projects undertaken. Implement rigorous testing methodologies to ensure high-quality deliverables.
About You
- 4-year degree in Quantitative disciplines (Science, Technology, Engineering, Mathematics) or equivalent experience
- Masters in computer science or equivalent industry experience
- Over 6 plus years of experience in end-to-end application development, data exploration, data pipelining, API design, optimization of model latency in production environments at scale.
- Experience in working with image, text data, embeddings, building & deploying vision models, integrating with Gen AI services
- Strong expertise in Machine Learning frameworks such as TensorFlow, PyTorch or similar libraries; experience with Generative Adversarial Networks or Diffusion Models is desirable
- Hands-on experience in libraries like NumPy, SciPy, Pandas, OpenCV, SpaCY, NLTK is expected
- Experience in optimizing ML model performance using techniques such as hyperparameter tuning, feature selection, and distributed training
- Good understanding of Big Data and Distributed Architecture- specifically Hadoop, Hive, Spark, Docker, Kubernetes and Kafka
- Experience working on GPUs is preferred
- Proven track record of optimizing machine learning models for performance improvements across various platforms including cloud services (AWS, Google Cloud Platform)
- Expertise in MLOps frameworks and hands on experience in MLOps tools like Kubeflow, MLFlow or Sagemaker
- Deep understanding of system architecture principles related to scalability and robust application design.
- Excellent communication, problem-solving skills combined with strong analytical abilities.
- Proven leadership skills with experience mentoring junior engineers effectively