About ShellKode :

ShellKode is a born-in-the-cloud company that specializes in Modernization, Security, Data, Generative AI, and AI/ML. Our industry-specialized solutions and services help organizations realize tangible business value and drive transformative growth. Our offerings span Financial Services, Retail, Logistics, and Healthcare with over 80 specialized industry solutions across data and GenAI.

What is the role about?

We are looking for an experienced Machine Learning Lead to drive end-to-end AI/ML initiatives, from problem definition to production deployment. This role combines technical leadership, architecture design, and hands-on model development, ensuring scalable and business-aligned ML solutions. You will lead a team of data scientists and engineers while collaborating with cross-functional teams to build impactful AI-driven products.

Key Responsibilities :

Technical Leadership :

- Lead the design and development of scalable machine learning solutions.

- Define ML architecture, model selection, and feature engineering strategies.

- Guide teams on best practices in model development, evaluation, and deployment.

- Review code, models, and pipelines to ensure high-quality delivery.

Model Development & Optimization :

- Build and deploy models for :

1. Customer churn prediction,

2. Recommendation systems,

3. Demand forecasting,

4. Customer segmentation.

- Optimize models for :

1. Accuracy,

2. Performance,

3. Scalability.

Data & Feature Engineering :

- Design robust data pipelines for : 1. Data ingestion, 2. Feature engineering, 3. Data validation.

- Handle large-scale structured and unstructured datasets.

MLOps & Deployment :

- Lead production deployment using :

1. AWS SageMaker / Lambda / APIs,

2. Docker, CI/CD pipelines.

- Implement :

1. Model monitoring,

2. Retraining pipelines,

3. Versioning and experiment tracking (MLflow).

Business Collaboration :

- Translate business problems into ML solutions.

- Work with stakeholders to define KPIs and success metrics.

- Drive data-driven decision-making across teams.

Team Management :

- Mentor junior and mid-level data scientists.

- Conduct technical reviews and knowledge-sharing sessions.

- Build a high-performance ML team.

Must-Have Criteria :

- 6+ years of experience in AI/ML or Data Science.

- Strong expertise in :

1. Machine Learning (supervised & unsupervised),

2. Feature engineering & model tuning/optimization.

- Proficiency in :

1. Python (NumPy, Pandas, Scikit-learn),

2. At least one : TensorFlow / PyTorch.

- Strong understanding of :

1. Probability & Statistics,

2. Linear Algebra,

3. Data Mining Concepts and Problem understanding and Solving Skills.

- Experience with :

1. Large-scale data processing,

2. SQL and databases (PostgreSQL, MySQL, MongoDB),

3. Hands-on experience in deploying ML models to production,

4. Strong debugging and optimization skills.

Preferred Skills :

- Experience with a wide range of machine learning use cases, including but not limited to :

1. Recommendation systems,

2. Time series forecasting,

3. Customer analytics (churn prediction, segmentation, CLV),

4. Classification and regression problems,

5. Anomaly detection and fraud detection,

6. NLP use cases (text classification, information extraction),

7. Demand forecasting and inventory optimization,

8. Personalization and targeting models.

- Ability to quickly understand and adapt ML solutions to new business problems across domains.

- MLOps tools : 1. MLflow, 2. Docker, 3. Kubernetes.

- Cloud platforms : 1. AWS (SageMaker, S3, Lambda).

- Experience in building : 1. REST APIs for ML inference.

- Knowledge of : 1. Data visualization (Power BI, matplotlib, seaborn).

Leadership & Soft Skills :

- Strong problem-solving mindset.

- Ability to lead and mentor teams.

- Excellent communication with both technical and business stakeholders.

- Experience handling end-to-end project ownership.

Preferred Qualifications :

- Bachelors/Masters degree in : 1. Computer Science, 2. Data Science, 3. Mathematics, 4. Statistics, 5. Engineering.

- Relevant certifications in AI/ML or Cloud (AWS/Azure/GCP).

Good to Have :

- Experience in GenAI / LLM-based systems.

- Knowledge of : 1. RAG pipelines, 2. Prompt engineering.

- Experience in building and deploying ML solutions across multiple industry domains, including but not limited to :

1. Retail (recommendation systems, demand forecasting, customer segmentation),

2. Healthcare (clinical data analysis, predictive diagnostics, operational optimization),

3. Financial Services (fraud detection, risk modeling, credit scoring),

4. E-commerce and Digital Platforms,

5. Logistics and Supply Chain,

6. Telecom and Customer Engagement platforms.

- Ability to understand domain-specific challenges and translate them into scalable, data-driven ML solutions.