Machine Learning Architect - Python Programming (8-10 yrs)

Job Description :

We are seeking an innovative and experienced Machine Learning Architect to design and implement state-of-the-art machine learning solutions that address complex business challenges. The ideal candidate will possess a strong technical background, leadership skills, and a passion for advancing AI-driven technologies.

Key Responsibilities :

- Architecture Design : Develop and oversee scalable, end-to-end machine learning pipelines, including data collection, preprocessing, training, evaluation, deployment, and monitoring.

- Strategic Leadership : Define the ML architecture roadmap, ensuring alignment with business goals and industry best practices.

- Model Development : Research, design, and implement advanced machine learning models and algorithms for real-world applications.

- Cross-functional Collaboration : Work closely with data scientists, engineers, and business stakeholders to identify requirements and deliver impactful solutions.

- Technology Integration : Implement ML solutions into cloud platforms (AWS, GCP, Azure) or on-premises systems, ensuring high availability and performance.

- Performance Optimization : Continuously evaluate and improve model accuracy, scalability, and deployment efficiency.

- Mentorship and Guidance: Provide technical leadership and mentoring to team members, fostering a culture of innovation and excellence.

Qualifications :

- Educational Background: Bachelor's/Master's degree in Computer Science, Engineering, Mathematics, or related fields. Ph.D. preferred.

- Experience: Proven experience in machine learning, data science, and AI systems architecture, with a track record of deploying production-grade ML solutions.

Technical Expertise :

- Proficiency in ML frameworks and libraries (TensorFlow, PyTorch, Scikit-learn, etc.).

- Strong programming skills in Python and experience with frameworks like Spark or Kafka.

- Expertise in cloud platforms (AWS, GCP, Azure) and containerized environments (Docker, Kubernetes).

- Solid understanding of big data technologies (Hadoop, Hive, etc.).

- Soft Skills : Excellent communication, problem-solving, and stakeholder management abilities.

- Bonus Skills : Knowledge of deep learning, reinforcement learning, and NLP technologies.