The AI / ML Engineer is responsible for designing, developing, and deploying machine learning models and AI-driven solutions to solve complex business problems. This role bridges the gap between theoretical AI research and practical implementation, ensuring scalable, ethical, and impactful applications of artificial intelligence and machine learning.

Algorithm Design & Training:

• Must have a strong understanding of different types of machine learning models (e.g., supervised/unsupervised learning, deep learning, NLP, computer vision).
• Must be familiar with hyperparameter tuning techniques to optimize model performance (accuracy, latency, scalability).

Prototyping & Experimentation:

• Build proof-of-concept implementations around different models to validate hypotheses and demonstrate business value.
• Stay updated on cutting-edge AI/ML techniques (e.g., transformers, generative AI, AI agents, Agentic vs. Semantic Chunking, Reinforcement learning)

Data Preparation:

• Be able to collaborate with data engineers to ensure high-quality, labeled datasets for training and validation.
• Must be familiar with the concepts of bias, noise and incompleteness of data and be able to implement processes to clean, pre-process, and transform data to address such challenges.

Pipeline Development:

• Design robust data ingestion, processing, and validation pipelines to support model training and inference.

Production Integration:

• Deploy models into production using best practices (e.g., containerization, REST APIs, edge deployment).
• Be familiar with the concepts of model drift, data quality, and performance degradation and be able to suggest monitoring techniques.

MLOps Practices:

• Collaborate with DevOps to automate CI/CD pipelines for model retraining and updates.
• Ensure scalability, security, and compliance in production systems.

Cross-Functional Alignment:

• Partner with Product Managers to translate business needs into technical requirements.
• Work with software engineers to integrate AI/ML solutions into existing systems.

Stakeholder Communication:

• Explain complex AI/ML concepts to non-technical audiences (e.g., executives, clients).
• Document model behavior, limitations, and ethical considerations.

Research & Innovation:

• Evaluate emerging tools, frameworks, and research papers to improve existing solutions.
• Experiment with techniques like AutoML, federated learning, or AI ethics frameworks.

Qualifications & Skills

Required Experience:

• 2+ years in AI/ML development, with hands-on experience deploying models to production.
• Proficiency in Python and libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Keras).

Technical Skills:

• Strong grasp of statistics, linear algebra, and optimization techniques.
• Experience with data manipulation (Pandas, NumPy) and big data tools (Spark, SQL).
• Familiarity with AWS cloud platform and MLOps tools like SageMaker.

Soft Skills:

• Problem-solving mindset with a focus on business impact.
• Strong communication and storytelling skills for technical and non-technical audiences.