AI Engineer Manager

Tasks

• Apply prompt engineering
• Automate MLOps LLMOps lifecycle
• Build agentic frameworks
• Build scalable inference infrastructure
• Create AI evaluation frameworks
• Design APIs microservices and orchestration
• Design and build RAG systems
• Design feasibility assessments
• Develop LLM-powered solutions
• Develop solution proposals
• Identify model failures
• Implement CI CD pipelines for AI and ML
• Lead AI project delivery end to end
• Mentor engineers
• Optimize latency cost reliability scalability
• Run structured experiments

Perks/Benefits

• All the equipment needed
• Anniversary gifts
• Birthday gifts
• English lessons
• Flexible working options
• Learning opportunities
• Lunchevery day
• Mentoring and development opportunities
• Snacks and beverages
• Study plans courses and certifications

Skills/Tech-stack

API Design | AWS | Azure | CI/CD | Chunking | Cost Optimization | Docker | Embeddings | Few-Shot Learning | Few-shot | GCP | Git | Inference Optimization | Kubernetes | LLM Evaluation | LLMOps | Language Models | Large Language Models | Latency optimization | MLOps | MLflow | Microservices | Observability | Prompt engineering | Python | RAG | Reranking | Retrieval | Security | Structured Output | Weights and Biases