AI/ML Engineer

Role Summary:

The AI/ML Engineer is responsible for designing, developing, and deploying machine learning and artificial intelligence models that power intelligent features across ARVO's platforms. This role focuses on building scalable data-driven solutions for personalization, prediction, and automation by leveraging modern ML frameworks, data pipelines, and cloud-based model deployment. The engineer collaborates closely with product, engineering, and analytics teams to translate business problems into technical solutions that improve learning outcomes, operational efficiency, and user experience.

Key Responsibilities and Duties Core Functional Responsibilities

a.     Design, develop, and optimize machine learning and deep learning models to address key business and educational challenges.

b.     Collect, preprocess, and analyze large datasets for model training, validation, and evaluation.

c.     Implement scalable data pipelines and model deployment workflows using cloud and containerized environments.

d.     Collaborate with data engineers to ensure data integrity, versioning, and model reproducibility.

e.     Perform feature engineering, hyperparameter tuning, and model performance optimization using systematic experimentation.

f.      Develop and integrate AI-driven recommendations, predictions, and automation capabilities within ARVO's digital ecosystem.

g.     Leverage NLP, computer vision, and reinforcement learning techniques where applicable to enhance content and user interaction.

h.     Ensure model interpretability, fairness, and compliance with data privacy and ethical AI standards.

i.      Continuously research and evaluate emerging AI/ML technologies, frameworks, and methodologies to drive innovation.

j.      Document model architecture, performance metrics, and deployment workflows for maintainability and scalability.

Core Leadership Responsibilities (Applicable on E-3 and above)

a.     Team Management: Mentor and guide junior engineers and data scientists in AI/ML practices, ensuring technical growth and adherence to standards.

b.     Time Management: Plan, prioritize, and oversee AI/ML project milestones to ensure timely delivery of models and solutions.

c.     Communication: Effectively communicate complex technical insights to non-technical stakeholders, fostering cross-functional understanding.

d.     Conflict Management: Address technical or resource conflicts constructively to maintain productivity and team alignment.

Key Result & Performance Indicators Delivery and Quality

a.     Develop and deploy high-quality AI/ML models that meet accuracy, latency, and scalability benchmarks.

b.     Ensure reproducibility, version control, and documentation of all ML experiments and model releases.

c.     Contribute to continuous improvement of AI development processes, automation pipelines, and monitoring systems.

Leadership Metrics (Applicable on E-3 and above)

a.     Leadership and Team Development: Build an environment of learning and innovation through knowledge sharing and mentorship.

b.     Collaboration and Communication: Maintain clear alignment with data, engineering, and business teams to achieve measurable outcomes.

c.     Time and Resource Management: Manage project scope, resources, and delivery timelines effectively.

Innovation and Continuous Improvement: Lead initiatives in adopting new algorithms, frameworks, and research to enhance AI capabilities.

d.     Conflict and Risk Management: Mitigate technical risks, manage dependencies, and ensure smooth execution of AI projects.

e.     Ownership and Accountability: Take end-to-end responsibility for deployed models, from conception to monitoring and retraining.

Key Skills & Competencies Technical Skills:

a.     Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-learn.

b.     Experience with data preprocessing, feature engineering, and model lifecycle management (MLflow etc.).

c.     Strong understanding of statistical modeling, machine learning algorithms, and deep learning architectures.

d.     Familiarity with cloud services (Azure etc.) for model training and deployment.

e.     Experience with data manipulation tools (Pandas, NumPy, SQL) and visualization libraries (Matplotlib, Seaborn).

f.      Knowledge of API development and integration for embedding AI models into production systems.

g.     Understanding of NLP, computer vision, or recommendation systems is a plus.

Soft Skills:

a.     Strong analytical and problem-solving mindset with attention to detail.

b.     Excellent communication and collaboration skills with cross-functional teams.

c.     Adaptable and open to new research, technologies, and iterative experimentation.

d.     Effective time management and ability to handle multiple projects simultaneously.

e.     Commitment to ethical AI practices and data privacy standards.

Qualification / Education / Experience

I Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related field.

II Minimum 2–3 years of experience in AI/ML model development and deployment for E-1/E-2 roles.

III Minimum 4–5 years of experience in applied machine learning or AI systems for E-3 and above roles.

IV
Hands-on experience with at least one cloud ML platform (AWS SageMaker, Azure ML) is required.