About the Role

We are seeking a highly skilled AI Engineer to operationalize machine learning models and deploy them into scalable, production-grade systems. This role focuses on integrating AI solutions including Natural Language Processing (NLP) and Computer Vision models into real-world applications while ensuring performance, reliability, and scalability.

You will work closely with data scientists, backend engineers, and DevOps teams to bridge the gap between research and production.

Key Responsibilities

1. AI Model Integration

• Deploy and operationalize ML models (NLP, Computer Vision) into production environments
• Convert research models into scalable APIs and services
• Optimize models for latency, throughput, and reliability
• Implement model versioning, testing, and monitoring systems

2. Production & Scalability

• Design and build AI-powered microservices
• Ensure models meet performance benchmarks and scalability requirements
• Implement load balancing, caching, batching, and inference optimization strategies
• Monitor production models for drift, accuracy degradation, and performance issues

3. Infrastructure & Deployment

• Deploy AI services using containerization tools (Docker, Kubernetes)
• Collaborate with DevOps teams to integrate AI workflows into CI/CD pipelines
• Work with cloud platforms (AWS, GCP, Azure) for model hosting and scaling
• Manage GPU and accelerated computing environments where applicable

4. Collaboration & Optimization

• Work with data scientists to refine model performance for production use
• Improve inference pipelines and preprocessing workflows
• Ensure secure, reliable, and compliant AI system implementation
• Document system architecture and deployment processes

Required Qualifications

• Bachelors or Masters degree in Computer Science, Artificial Intelligence, Machine Learning, or related field
• 3+ years of experience in ML engineering or AI systems deployment
• Strong proficiency in Python
• Experience deploying NLP and/or Computer Vision models
• Proficiency with AI/ML frameworks including: TensorFlow, PyTorch, Scikit-learn
• Strong experience with data handling and processing libraries such as: Pandas, NumPy
• Experience building scalable APIs for model inference
• Strong understanding of system performance optimization

Preferred Qualifications

• Experience with MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI, etc.)
• Experience with model monitoring and observability tools
• Familiarity with distributed systems and microservices architecture
• Experience working with LLMs and generative AI systems
• Knowledge of data engineering pipelines

Key Competencies

• Strong problem-solving and analytical skills
• Ability to bridge research and production environments
• Systems-thinking mindset
• Strong collaboration and communication skills