AI/ML Expert

Job Description – AI/ML Expert

Position Overview

We are seeking experienced AI/ML professionals from India to join our technology team

in Malaysia. This opportunity is ideal for accomplished AI/ML engineers, data scientists,

machine learning specialists, and applied AI professionals with 5–10 years of relevant

experience who are interested in working on enterprise-grade AI solutions and

contributing to the development of intelligent, scalable, and data-driven applications.

The selected candidate will work closely with engineering, product, data, and business

teams to identify opportunities for applying Artificial Intelligence and Machine Learning to

real-world business challenges.

The exact designation may be aligned with the candidate’s expertise and experience, such

as AI/ML Engineer, Machine Learning Engineer, AI Engineer, Data Scientist, Applied AI

Engineer, or Senior AI/ML Specialist.

Key Responsibilities

• Design, develop, implement, and maintain AI and Machine Learning solutions for

business and technology use cases.

• Analyze complex business problems and translate them into suitable AI/ML

approaches.

• Develop and optimize machine learning models using appropriate algorithms,

frameworks, and techniques.

• Work with structured and unstructured datasets to identify patterns, trends, and

actionable insights.

• Perform data preparation, feature engineering, model training, validation,

evaluation, and optimization.

• Develop production-ready AI/ML pipelines and integrate models into enterprise

applications and platforms.

• Apply techniques such as supervised and unsupervised learning, deep learning,

natural language processing, computer vision, recommendation systems,

forecasting, and predictive analytics, depending on project requirements.

• Explore and implement modern Generative AI and Large Language Model (LLM)

technologies where relevant.

• Develop solutions involving LLMs, prompt engineering, embeddings, vector

databases, Retrieval-Augmented Generation (RAG), AI agents, and model

orchestration, where applicable.

• Evaluate emerging AI technologies, frameworks, and models and recommend

appropriate solutions.

• Collaborate with software engineers to deploy AI/ML models into production

environments.

• Monitor model performance, accuracy, scalability, reliability, and data quality after

deployment.

• Identify opportunities to improve model performance through experimentation,

tuning, and continuous learning.

• Design and implement appropriate approaches for model explainability, security,

privacy, and responsible AI.

• Participate in architecture discussions and contribute to the design of scalable AI-

enabled applications.

• Work with cloud platforms and modern MLOps practices for model deployment and

lifecycle management.

• Prepare technical documentation covering models, methodologies, architectures,

experiments, and deployment processes.

• Mentor junior engineers/data scientists and contribute to the development of AI/ML

engineering best practices.

• Stay current with developments in Artificial Intelligence, Machine Learning, Deep

Learning, Generative AI, LLMs, and MLOps.

Required Technical Skills

Candidates should have strong practical experience in several of the following areas:

Programming & Data

• Strong proficiency in Python.
• Experience with SQL and relational/non-relational databases.
• Strong understanding of data structures, algorithms, statistics, and data analysis.
• Experience with libraries/frameworks such as NumPy, Pandas, Scikit-learn,

TensorFlow, PyTorch, or equivalent.

Machine Learning

• Strong understanding of machine learning concepts and algorithms.
• Experience with model development, training, validation, evaluation, and

optimization.

• Hands-on experience with regression, classification, clustering, recommendation,

forecasting, or other relevant ML techniques.

• Knowledge of feature engineering, model selection, hyperparameter optimization,

and performance evaluation.

Deep Learning / AI

Experience in one or more of:

• Neural networks and deep learning.
• Natural Language Processing (NLP).
• Computer Vision.
• Speech/Audio AI.
• Time-series analysis.
• Recommendation systems.
• Predictive analytics.
• Generative AI.

Generative AI / LLM

Exposure to or hands-on experience with:

• Large Language Models (LLMs).
• Prompt engineering.
• Embeddings and semantic search.
• Retrieval-Augmented Generation (RAG).
• Vector …