AI Engineer

3 days ago

Delhi, India renloc Full time
Key Responsibilities
AI-Powered Content and Question Generation
Develop Advanced AI Models: Utilize OpenAI's GPT-4 and other leading LLMs to generate diverse and high-quality educational content, including complex questions and assessments across various subjects and difficulty levels.
Fine-Tune LLMs: Customize large language models on domain-specific datasets to create adaptive and subject-focused question banks.
Innovate in Content Creation: Implement AI-driven solutions for automating the generation of assessments, ensuring content aligns with learning objectives and standards.
Workflow Automation and Optimization
Automate Educational Workflows: Design AI solutions to streamline processes such as grading, feedback generation, and content curation, significantly reducing manual effort for educators.
Integrate Intelligent Tools: Deploy AI-powered automation tools to enhance accuracy, efficiency, and scalability in educational workflows.
Personalized Learning and AI Recommendations
Build Adaptive Learning Systems: Develop algorithms for personalized content recommendations, leveraging user performance data to suggest tailored learning pathways.
Enhance User Engagement: Implement real-time AI feedback mechanisms to improve learner outcomes and engagement through personalized insights and suggestions.
Advanced NLP and Model Integration
Leverage Cutting-Edge NLP: Utilize advanced NLP frameworks like Hugging Face Transformers and OpenAI APIs for content analysis, summarization, and enhancement.
Implement AI in User Interaction: Integrate AI-driven features such as chatbots, virtual assistants, and intelligent tutoring systems to enrich the user experience.
AI Infrastructure and Scalability
Deploy Scalable AI Solutions: Use frameworks like LangChain for orchestrating complex LLM workflows and Pinecone for efficient vector-based data retrieval.
Optimize AI Pipelines: Implement scalable AI architectures using containerization (Docker), orchestration (Kubernetes), and serverless computing to ensure robust performance.
Data Engineering and Management
Handle Large Datasets: Preprocess, clean, and structure extensive datasets for training and refining AI models.
Develop ETL Pipelines: Create efficient data pipelines to integrate multiple data sources, ensuring data integrity and accessibility.
Optimize Databases: Utilize SQL and modern database technologies, including vector databases, for high-performance data operations.
Cloud Infrastructure and Deployment
Leverage Cloud Platforms: Deploy AI models on cloud services like Microsoft Azure AI, AWS SageMaker, or Google Cloud Vertex AI for scalability and reliability.
Ensure Robust Deployment: Use CI/CD pipelines and infrastructure-as-code tools to automate deployment processes and maintain system integrity.
Collaboration and Continuous Innovation
Cross-Functional Collaboration: Work closely with product managers, instructional designers, and software developers to align AI initiatives with business goals.
Stay Ahead of AI Trends: Continuously research and implement emerging AI technologies, such as generative AI and multimodal models, to keep the platform cutting-edge.
Required Qualifications
Technical Expertise
Advanced AI Proficiency: Extensive experience with OpenAI APIs (including GPT-4 or newer) and practical applications in content generation and workflow automation.
Programming Skills: Expert-level proficiency in Python and familiarity with frameworks like TensorFlow and PyTorch.
Database Knowledge: Strong experience with SQL and modern database architectures, including vector databases like Pinecone or Weaviate.
AI and Machine Learning Skills
LLM Fine-Tuning: Proven ability to fine-tune large language models and transformer architectures for specific use cases.
NLP Mastery: Deep understanding of natural language processing techniques and tools, such as Hugging Face Transformers, for advanced text manipulation.
Cloud and Infrastructure
Cloud Deployment: Experience deploying AI solutions on cloud platforms (Azure, AWS, or Google Cloud) with a focus on scalability and performance.
Containerization and Orchestration: Proficiency in using Docker and Kubernetes for deploying and managing AI services.
CI/CD Practices: Knowledge of continuous integration and deployment pipelines to streamline development workflows.
Workflow Automation
Pipeline Development: Experience with automation tools like Apache Airflow or Prefect for building and managing data pipelines.
Integration Skills: Ability to integrate AI models into existing workflows and systems seamlessly.
Certifications
Microsoft Certified: Azure AI Engineer Associate (Required).
Additional certifications such as AWS Certified Machine Learning – Specialty or Google Professional Machine Learning Engineer are a plus.
Preferred Qualifications
EdTech Experience: Familiarity with educational technologies, Learning Management Systems (LMS), and standards like SCORM and LTI.
Responsible AI Practices: Understanding of AI ethics, bias mitigation, and responsible AI guidelines to ensure fair and ethical AI deployment.
Performance Evaluation: Expertise in A/B testing, user behavior analysis, and AI system performance metrics.
Frontend Integration: Basic knowledge of frontend technologies (e.g., React, Angular) for integrating AI features into the user interface.
Skills
Collaborative Mindset: Excellent communication skills with the ability to work effectively in cross-functional teams.
Innovative Thinking: Proactive in adopting emerging AI technologies and finding creative solutions to complex problems.
Passion for Education: Deep commitment to enhancing educational experiences through innovative AI applications.
Problem-Solving Skills: Strong analytical abilities to diagnose issues and optimize AI models and workflows.

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