Design intelligent workflows leveraging retrieval systems, embeddings, and memory architectures.
Evaluate and integrate frontier AI models across multiple providers.
Develop scalable backend services and APIs supporting AI-driven applications.
Implement asynchronous orchestration and distributed processing pipelines.
Optimize inference stacks for latency, cost-efficiency, and performance.
Design data pipelines for continuous model evaluation and retraining.
Deploy AI applications using containerization and modern CI/CD workflows.
Implement infrastructure automation and secure cloud architectures.
Establish monitoring, logging, and drift detection mechanisms for AI services.
Ensure high availability and reliability of multi-tier applications.
Prototype and validate AI concepts through structured experimentation.
Conduct performance benchmarking across latency, accuracy, and system stability metrics.
Translate research findings into production-ready systems.
Maintain documentation for reproducibility and governance.
Integrate perception systems combining sensor data for real-time decision-making.
Optimize AI models for edge devices and resource-constrained environments.
Develop embedded firmware integrations for intelligent hardware systems.
Lead cross-functional engineering initiatives involving AI, frontend, and cloud teams.
Mentor junior engineers and foster rapid experimentation cycles.
Communicate technical trade-offs and system impact clearly to stakeholders.
Contribute to product strategy through measurable AI-driven insights.
Requirements
3+ years of experience in AI Engineering with supporting and delivering.
Strong programming expertise in Python and C/C++
Embedded C, FreeRTOS, REST APIs, IoT Systems, Linux
Experience building LLM-based systems and multi-agent architectures
Hands-on experience of cloud platforms (AWS, Azure, or GCP)
Experience with Docker and CI/CD pipelines
Strong experience in AI and Machine Learning
Experience in CRM & Project Delivery
Strong hands-on experience in L2 support, incident resolution, root-cause analysis, monitoring design, and performance optimization across distributed systems.
Understanding of distributed systems and REST-based integrations
Experience ML frameworks such as TensorFlow or PyTorch
Strong experience in debugging, monitoring, and incident resolution capabilities
Good knowledge in professional skills such as Incident Analysis & Root Cause Identification, Technical Leadership, Clear Communication, Cross-Team Collaboration
