AI Engineer

Job Description

Title: AI Engineer

Company Name: Troubleshooters Limited

Vacancy: 1

Age: 25 to 40 years

Job Location: Dhaka

Salary: Negotiable

Experience:

• 2 to 4 years
• The applicants should have experience in the following business area(s): Software Company,IT Enabled Service,Artificial Intelligence (AI) Startup

• Bachelor of Science (BSc) in Computer Science & Engineering

• 2 to 4 years
• The applicants should have experience in the following business area(s): Software Company,IT Enabled Service,Artificial Intelligence (AI) Startup

• Age 25 to 40 years

Experience Requirements:

• 2–4+ years of experience in applied AI or machine learning roles
• Strong experience working with open-source foundation models (e.g., Hugging Face, Meta AI, etc.)
• Proficiency in Python, PyTorch, TensorFlow, and tools like Transformers, LangChain, or LlamaIndex
• Experience managing full AI lifecycle from data collection to model deployment

Job Context: NECX, in partnership with Troubleshoot Limited, is hiring a forward-thinking AI Engineer to lead the development of intelligent systems that power secure, real-time platforms. This role is perfect for someone passionate about cutting-edge AI models, fine-tuning open-source architectures, and deploying scalable inference systems. You’ll join a lean, impact-driven team working to build AI-powered solutions for organizations that handle sensitive and mission-critical data.

Job Responsibilities:

• Implement & Fine-Tune Models: Identify, adapt, and fine-tune open-source AI models (e.g., LLaMA, Mistral, T5) for domain-specific applications
• Optimize AI for Inference: Deploy and manage AI models with a focus on speed, scalability, and resource efficiency
• Data Pipeline Development: Build robust pipelines for data collection, preprocessing, and transformation for AI model training and deployment
• AI Agent Architecture: Design and implement advanced AI agent systems, including Mixture of Experts (MoE) and multi-agent setups
• API Development: Build and integrate REST or gRPC APIs to connect AI systems with frontend/backend platforms
• Infrastructure Management: Maintain high-performance hosting environments using Docker, cloud services (AWS/GCP/Azure), and MLOps tools
• Monitoring & Maintenance: Track AI system health, latency, and performance using modern DevOps and MLOps pipelines