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AI / ML engineer

Overview

We are seeking an AI/ML Engineer to help design and deliver production-grade artificial intelligence solutions for our clients. You will work across the full machine learning lifecycle — from research and prototyping to deployment and monitoring — on projects spanning natural language processing, computer vision, predictive analytics, and generative AI.

Key Responsibilities

Design, develop, and deploy production-ready machine learning models and AI systems
Build advanced NLP solutions including text classification, sentiment analysis, conversational AI, and LLM integration
Develop computer vision pipelines for image and video analysis, object detection, and recognition
Partner with data scientists, engineers, and clients to translate business problems into ML solutions
Optimize models for production environments with a focus on latency, throughput, and cost efficiency
Research and evaluate emerging AI/ML technologies, frameworks, and architectures
Build robust, scalable ML pipelines for data processing, training, and inference
Conduct rigorous model evaluation, A/B testing, and performance benchmarking
Document ML workflows, model architectures, and experimental results to a publishable standard
Troubleshoot and debug ML systems in production environments
Stay current with AI research and contribute to internal knowledge sharing
Mentor junior engineers and help establish ML engineering best practices

Requirements

3+ years of hands-on ML/AI development experience with a demonstrable portfolio
Expert Python proficiency with deep experience in TensorFlow, PyTorch, or scikit-learn
Strong deep learning fundamentals including CNNs, RNNs, Transformers, and GANs
Practical NLP experience with transformer architectures, fine-tuning, and prompt engineering
Computer vision proficiency including classification, detection, and segmentation tasks
Experience with cloud ML services (AWS SageMaker, Google Cloud AI, or Azure ML) and MLOps tooling
Strong data preprocessing, feature engineering, and model evaluation expertise
Knowledge of model deployment, containerization (Docker), and serving frameworks
Familiarity with data engineering tools such as Pandas, NumPy, and Spark
Experience with experiment tracking tools (MLflow, Weights & Biases, or DVC)
Solid mathematical foundation in statistics, linear algebra, and optimization
Published research, open-source contributions, or a portfolio of diverse ML projects is a strong advantage

What We Offer

Competitive salary with performance-based bonuses
Fully remote — work from anywhere in the world
Annual learning budget for courses, conferences, and certifications
Flexible working hours with asynchronous communication
Opportunity to work on diverse, challenging projects across multiple industries
Career growth and leadership opportunities within a growing organization

Job Overview

Offered Salary

Career Level

Industry
IT & Software Developmentit

Experience

Qualification