Senior Artificial Intelligence Engineer

Role Overview:

Senior AI Engineer

This is a hands-on leadership role,not a people-management-only position. You will write code, ship models, and raise the technical bar of the AI team while collaborating closely with product and engineering leadership.

What You Will Do:

Lead Core AI Systems

• Architect and build AI systems for:
• Automated exam evaluation (objective and subjective answers)
• Rubric-based scoring, normalization, and consistency checks
• NLP-based answer understanding, similarity scoring, and feedback generation
• Design scalable, reliable AI pipelines from data ingestion to inference and monitoring.
• Evaluate, fine-tune, and deploy modern NLP models and LLMs for education use cases.

Own AI Quality, Performance & Scale:

• Set standards for model evaluation, testing, and production readiness.
• Balance trade-offs between accuracy, latency, cost, and explainability.
• Implement monitoring, alerting,and retraining strategies for production models.
• Address model drift, bias, and edge cases in high-stakes evaluation systems.

Drive Execution & Mentorship:

• Take ownership of high-impact AI initiatives from concept to launch.
• Mentor junior and mid-level AI engineers through code reviews and technical guidance.
• Collaborate with product managers to translate educational and business requirements into AI solutions.
• Partner with backend engineers to integrate AI systems into scalable services.

Innovation & Strateg:

• Identify opportunities where AI can create competitive advantage.
• Prototype and validate new AI-powered features and products.
• Stay current with advancements in NLP, LLMs, and applied AI, and apply them pragmatically.

Required Qualifications :

• Bachelors or Masters degree in

  Computer Science, AI, Data Science, or related field

  .
• 6+ years of experience building and deploying AI/ML systems in production.
• Strong expertise in

  Python

  and modern ML frameworks.
• Deep understanding of

  NLP techniques

  , including transformers, embeddings, and semantic similarity.
• Hands-on experience with

  LLMs

  , including fine-tuning, prompt engineering, and evaluation.
• Proven experience deploying models as scalable services (APIs, microservices).
• Strong system design and problem-solving skills.

Strongly Preferred Qualifications:

• Experience building AI systems in

  education, assessment, or other high-stakes decision domains

  .
• Experience with

  MLOps

  tools and practices (MLflow, Airflow, CI/CD for ML).
• Familiarity with vector databases, retrieval-augmented generation (RAG), and ranking systems.
• Cloud experience (AWS, GCP, or Azure).
• Experience handling large datasets and high-throughput inference systems.
• Strong opinions on AI quality, reliability, and engineering rigor.