AI Engineer

We are seeking a highly skilled AI Engineer to design, develop, deploy, and maintain advanced AI/ML solutions for a US-based client. This is an individual contributor role, meaning the selected candidate will be responsible for handling the project end to endfrom ideation and architecture to deployment, monitoring, and iteration.

The ideal candidate is someone who can think independently, build production-grade AI systems, communicate clearly with stakeholders, and take complete ownership of delivering solutions on time and at high quality.

• Own the full lifecycle of AI/ML projects independentlyfrom discovery to deployment
• Build and optimize machine learning, deep learning, and LLM-based models
• Develop end-to-end ML pipelines: data collection, preprocessing, modeling, evaluation, and monitoring
• Create and manage deployments using AWS/GCP/Azure and container tools (Docker, Kubernetes)
• Integrate models into production using APIs, microservices, or custom architectures
• Monitor model performance and maintain long-term model health
• Work directly with US-based stakeholders to align requirements and deliverables
• Research and apply the latest advancements in AI, LLMs, and generative technologies
• Ensure scalability, efficiency, and performance across all AI systems

• 3+ years of experience as an AI Engineer / ML Engineer / Data Scientist
• Strong proficiency in Python, ML frameworks (PyTorch, TensorFlow, Scikit-learn)
• Hands-on experience with LLMs, NLP models, embeddings, vector databases, RAG, or fine-tuning
• Proven ability to deploy models to production environments
• Experience with cloud platforms (AWS, GCP, or Azure)
• Ability to independently manage complex, end-to-end projects
• Strong problem-solving, debugging, and optimization skills
• Experience working with US clients is a plus
• Comfortable in a hybrid working environment

• Experience with RAG frameworks like LangChain, LlamaIndex
• Familiarity with MLOps, CI/CD, orchestration tools
• Exposure to computer vision, multimodal models, or reinforcement learning
• Knowledge of quantization, GPU optimization, or model compression
• Open-source contributions in AI or ML

• Competitive salary + performance bonuses
• Hybrid work model
• Ownership of impactful, real-world AI projects
• Opportunity to work with advanced AI tools
• Career growth in high-level AI engineering
• Professional learning budget

1. Application Review
2. Screening Questions (Yes/No + Technical Background Check)
3. Technical Assignment (AI/ML Model or Coding Challenge)
4. Technical Interview (ML/AI Skills, Architecture, Deployment Experience)
5. Client Interview (US-Based Client Project Fit & Communication Skills)
6. Final Discussion & Offer