Highly skilled and experienced AI Development Lead to drive the design, development, and deployment of AI solutions. This role involves leading a team of AI/ML engineers.

Technical Skills :

- Programming Languages : Python (primary), R, Java (optional).

- AI/ML Frameworks : TensorFlow, PyTorch, Scikit-learn, Hugging Face.

- Data Engineering : Spark, Databricks, Airflow, SQL, Pandas.

- Cloud Platforms : Azure (preferred), AWS, or GCP.

- MLOps Tools : MLflow, Azure ML, Kubeflow, Docker, Kubernetes.

- Version Control & CI/CD : Git, GitHub Actions, Azure DevOps.

- APIs & Integration : RESTful APIs, FastAPI, Flask.

Required Qualifications :

- Bachelors or Masters degree in Computer Science, Data Science, or a related field.

- 6 - 10 years of experience in AI/ML development, with at least 2 years in a leadership role.

- Proven experience in deploying AI models to production environments.

- Strong problem-solving, communication, and team leadership skills.

Key Responsibility Areas (KRAs) :

(Expanded and more detailed as requested)

1. AI Solution Design & Architecture :

- Architect scalable and robust AI/ML solutions aligned with business requirements.

- Choose appropriate models, frameworks, and tools based on problem statements.

-Define model deployment strategies, cloud infrastructure, and integration patterns.

2. End-to-End Model Development :

- Oversee the full lifecycle of ML model development - data ingestion, preprocessing, model training, validation, and optimization.

- Ensure reproducibility, reliability, and scalability of developed models.

- Drive experimentation using state-of-the-art techniques including NLP, deep learning, computer vision, or generative AI.

3. Team Leadership & Mentorship :

- Lead a team of AI/ML engineers, data scientists, and MLOps engineers.

- Review code, provide technical guidance, and ensure adherence to best practices.

- Conduct skill development sessions, technical workshops, and knowledge-sharing activities.

4. Data Strategy & Engineering Coordination :

- Collaborate with Data Engineering teams to build efficient data pipelines.

- Define data requirements, perform data quality checks, and establish feature engineering standards.

- Optimize data processing workflows using Spark/Databricks.

5. MLOps & Production Deployment :

- Implement CI/CD pipelines for ML models using MLflow, Azure ML, or Kubeflow.

- Build automated workflows for model tracking, monitoring, retraining, and performance tracking.

- Ensure models are robust and scalable in production environments.

6. Model Monitoring & Performance Optimization :

- Set up monitoring dashboards and alerts for model drift, data drift, and performance degradation.

- Continuously evaluate model accuracy and recommend enhancements.

- Manage versioning, rollback strategies, and SLAs for model performance.

7. Cross-Functional Collaboration :

- Work closely with product managers, data engineers, and business stakeholders.

- Translate business challenges into AI-driven solutions.

- Present findings, insights, and solution recommendations to leadership teams.

8. Research & Innovation :

- Stay updated with the latest advancements in AI/ML, LLMs, generative AI, and cloud ML services.

- Identify opportunities to implement innovative solutions using new technologies.

- Create POCs, prototypes, and pilot AI solutions to validate new ideas.

9. Governance, Compliance & Documentation :

- Ensure all models comply with data security, privacy, and regulatory guidelines.

- Maintain comprehensive documentation of architecture, design decisions, and processes.

- Implement responsible AI practices and ethical AI standards.

10. Project Management & Delivery :

- Define project timelines, milestones, and resource allocation.

- Monitor progress, manage risks, and ensure on-time delivery of AI initiatives.

- Ensure clear communication of project status to stakeholders.