Qualifications:

1. Strong proficiency in developing and deploying AI/ML models, with expertise in areas such as Machine Learning, Natural Language Processing, Computer Vision, or Reinforcement Learning.

2. In-depth understanding of both discriminative and generative AI algorithms and architectures.

3. Expertise in prompt engineering and cutting-edge AI tools/frameworks.

4. Proven experience designing and implementing end-to-end AI/ML pipelines for production environments.

5. Familiarity with large language models (LLMs) and their applications.

6. Strong programming skills in Python, including expertise in data analysis libraries (Pandas, NumPy, Scikit-learn).

7. Hands-on experience with AI/ML frameworks such as TensorFlow, PyTorch, or Keras.

8. Proficient in SQL for data extraction and analysis.

9. Working knowledge of cloud platforms, preferably Google Cloud Platform (GCP).

10. Experience with data pipelines, ETL processes, and MLOps practices.

11. Experience of deployment frameworks (e.g., FastAPI, Streamlit) and containerization (e.g., Docker).
    

Preferred Qualifications:

1. Familiarity with GCP AI/ML services (e.g., Vertex AI) and advanced AI technologies like RAG (Retrieval-Augmented Generation), vector databases, and document embeddings.

2. Strong communication skills to explain technical concepts to technical and non-technical stakeholders.

3. Demonstrated experience working in cross-functional teams and aligning AI initiatives with organizational goals.

4. Leadership/participation in organizations (e.g., campus, NGOs, or professional groups such as DSI, DEI, FIM, YLI, etc.) showcasing organizational and team-building skills.

5. Proven ability to publish, present, or share technical insights through blogs, social media platforms, or conferences, contributing to team branding and personal growth.

6. Actively pursuing or possessing official certifications in AI/ML, data science, or cloud platforms (e.g., TensorFlow Developer Certificate, Google Cloud Professional Certification) or completion of recognized professional courses.

What will you do:

1. Collaborate closely with product, design, engineering, and business teams to understand data needs and develop AI solutions.

2. Research and develop advanced AI/ML models, including prototypes for complex business problems.

3. Implement and maintain AI/ML solutions for processes like document verification, credit scoring, and chatbot development.

4. Optimize and scale existing AI/ML models and workflows in production environments.

5. Lead AI/ML initiatives focusing on innovative technologies such as OCR engines and LLMs.

6. Document AI models, processes, and solutions to ensure reproducibility and knowledge sharing.

7. Drive the adoption of best practices in MLOps, including deployment, monitoring, and retraining of models.

8. Mentor team members and contribute to a collaborative, high-performance culture.