AI Engineer

• 2-3 years of experience in AI Engineering, with significant recent experience designing and deploying Gen AI and LLM-based solutions.
• Proven hands-on experience building end-to-end ML/DL pipelines
• Bachelor's or Master's program in Computer Science, Data Science, Machine Learning, or a related field.
• Solid grasp of probability, statistics, linear algebra, and algorithms.
• Exposure to LLM-powered agents, familiarity with RAG pipelines, as well as with tool/function calling, multi-step planning and orchestration.
• Proficiency in Python; familiarity with at least one ML/RL or deep-learning framework (PyTorch, TensorFlow, JAX).
• Experience with SQL (BigQuery, PostgreSQL) and pandas / Spark.
• Recommender Systems Exposure
• API Know-How: Understanding of REST services and how models are surfaced as endpoints.
• Testing & Documentation: Awareness of unit/integration testing for data pipelines and eagerness to learn experiment-driven development.
• Soft Skills: Clear communicator who thrives in a fast-paced, collaborative environment.

• Familiarity with real-time stream processing (Kafka, Flink)
• Exposure to AWS/GCP AI services
• Interest in LLM-based recommendation, embeddings, or content understanding.
• Basic knowledge of observability stacks (Prometheus, Grafana)
• Comfort with an additional backend language (Go, Node.js, or Ruby) for service integration.

As an AI & Content-Recommendation Engineer, you'll work with our Machine-Learning and Backend teams to design, train, and deploy the models and data pipelines that decide "what to watch next" on our Smart TV platform. You'll gain hands-on experience across the full ML lifecycle - from exploratory data analysis through online A/B testing - while shipping features used by millions of viewers worldwide.

• Design, build and deploy LLM-powered agents that improve recommendation and information-retrieval experiences.
• Prototype and train ranking/recommendation models from large-scale interaction logs.
• Design offline metrics and analyze results. Help set up or monitor online A/B tests; turn findings into iteration plans.
• Expose recommendation APIs and integrate them with our existing Go / Ruby services.
• Contribute to CI/CD pipelines for data & model versioning (GitHub Actions, Docker).
• Code Reviews & Collaboration: Participate in peer reviews; give and receive constructive feedback. Work closely with product owners and teammates to prioritize and scope tasks.
• Follow Agile Processes: Adhere to sprint ceremonies, ticketing workflows, and documentation practices.
• Monitor & Measure: Assist in establishing basic SLAs and KPIs for service performance; learn to track and report on these metrics.