AI Engineer
- Posted 2 months ago
- Be among the first 20 applicants
Job Description
Focus: AI-driven scoring, evaluation, and learning systems
Responsibilities
Responsibilities
- Design and implement Retrieval-Augmented Generation (RAG) pipelines for assessment evaluation and feedback
- Explore and apply Reinforcement Learning (RL) techniques to improve scoring, guidance, or adaptive assessments
- Work with multimodal data (text, structured outputs, interaction logs, etc.)
- Collaborate with product and engineering teams to productionize AI systems
- Strong experience with applied AI/ML systems
- Hands-on experience with RAG architectures and LLM-based pipelines
- Familiarity with RL concepts and practical implementations
- Ability to deploy and maintain AI systems in production environments
More Info
Key Skills
About Company
Skills:
Python, Tensorflow, Django, Machine Learning, Kafka, AWS, Angular, Pytorch, Kubernetes, Azure, Gcp, Docker, Flask, Responsible AI practices, Microsoft Copilot Studio
Skills:
Apis, Microservices, enterprise knowledge architectures, response governance, AI orchestration frameworks, orchestration patterns, Generative AI, Graph RAG, data pipelines, retrieval optimization, LLMs, evaluation grounding, tool calling, integration mechanisms, multi-agent architectures, knowledge layers, prompt engineering, vector stores, Agentic AI, RAG, reranking, hallucination mitigation, MCP context engineering
Skills:
static code analysis , MLops, Kubernetes, Python, Docker, Restful Apis, RAG architectures, Cloud Monitoring, GRPC, OpenTelemetry, microservice design patterns, Generative AI, token metering
Skills:
Tensorflow, Pytorch, ROS, Python, ArduPilot, reinforcement learning, Multi-Agent Reinforcement Learning, PX4, ROS2, Robotics Simulators
Skills:
Java, Python, Anthropic, AI applications, LLMs, cloud AI services, OpenAI, Azure AI, agent frameworks, RAG architectures, vector databases