Lead Engineer AI

Job highlights

• Bachelor s or master s degree in computer science,AI,Data Science,or related fields.
• 2+ years of experience in team leading
• Hands-on experience with ETL / ELT workflows,data transformations,and SQL (data modeling,normalization,optimization)

Key Responsibilities

• AI Model Development:
• Design, train, and deploy AI models for diverse use cases in Computer Vision, NLP, and Generative AI .
• Fine-tune and deploy open-source large language models (LLMs) and private models for EdTech applications.
• Implement RAG (Retrieval-Augmented Generation) systems combining retrieval with generative models.
• Leverage LangGraph and LangChain for orchestration, multi-step workflows, and stateful agent design.
• Data Engineering Integration:
• Develop and integrate real-time data pipelines and event-driven architectures using Kafka, Spark, or PySpark .
• Build secure, scalable, and cloud-native AI solutions using AWS Bedrock, SageMaker, Google Vertex AI, Azure ML (AML), Azure AKS , and related services.
• Implement MLOps/LLMOps pipelines for efficient deployment, monitoring, and scaling of AI models.
• AI Infrastructure Tools:
• Work with modern AI toolkits and frameworks such as Hugging Face Transformers, MLflow, and Databricks .
• Develop prompt engineering strategies and optimize LLMs for improved accuracy and relevance.
• Contribute to internal AI frameworks and toolkits for accelerated adoption across teams.
• Leadership:
• Mentor and guide junior engineers, fostering innovation and skill development.
• Collaborate with cross-functional teams to align AI strategies with business goals.
• Promote best practices in coding, testing, documentation, and model lifecycle management.

Requirements

• Experience:
• 5+ years in AI/ML system development and deployment in production environments.
• 2+ years of experience in team leading.
• Proven track record of designing AI systems in cloud environments and working with data-intensive pipelines .
• Technical Skills:
• Strong Python proficiency (async programming, multi-step workflows).
• Expertise in machine learning and deep learning frameworks: TensorFlow, PyTorch, Scikit-learn .
• Proficiency with tools like Hugging Face, LangChain, LangGraph, OpenAI API, and Bedrock API .
• Hands-on experience with ETL/ELT workflows , data transformations, and SQL (data modeling, normalization, optimization) .
• Knowledge of RAG architectures, Graph Databases, and MCP (Model Context Protocol) .
• Familiarity with CI/CD pipelines , containerization ( Docker, Kubernetes ), and cloud AI platforms ( AWS, GCP, Azure ).
• Domain Knowledge:
• Understanding of EdTech standards like LTI, xAPI, QTI , and SCORM.
• Familiarity with adaptive learning systems and educational content pipelines.
• Knowledge of workflows in K-12, higher education, and corporate learning environments is a strong plus.
• Soft Skills:
• Exceptional leadership and team mentoring abilities.
• Strong problem-solving skills and excellent communication for technical and non-technical stakeholders.
• Education:
• Bachelor s or master s degree in computer science, AI, Data Science, or related fields.