Generative AI Engineer

Role overview

Responsibilities

• check_circle Prototype and Deploy AI Solutions
• check_circle Rapidly prototype, iterate, and ship AI-powered experiences using the latest capabilities of LLMs, agent frameworks, and recommender systems.
• check_circle Architect and deploy ML/GenAI products on cloud platforms (AWS, GCP, or similar).
• check_circle Build and maintain end-to-end AI workflows including data ingestion, feature engineering, modeling, evaluation, and deployment.
• check_circle AI Engineering & Orchestration
• check_circle Design and manage ML orchestration frameworks (Airflow, Kedro, ZenML, Flyte, etc.) to ensure scalability and reproducibility.
• check_circle Integrate LLMs and data into autonomous, multi-step workflows.
• check_circle Critically review AI-generated code for correctness, performance, and engineering best practices.
• check_circle Cross-Functional Collaboration
• check_circle Partner with product, design, research, and data science teams to take ideas from concept to launch.
• check_circle Translate complex business problems into AI-driven solutions with measurable impact.
• check_circle Innovation & Best Practices
• check_circle Stay on top of cutting-edge AI research and open-source innovation, incorporating new tools and techniques into production.
• check_circle Promote responsible, ethical, and impactful AI practices.
• check_circle Share thought leadership and help build a strong engineering culture in applied AI.
• check_circle A full-stack AI prototyper with hands-on experience building projects with modern AI tools.
• check_circle Proficient in Python (plus experience with Java or Scala is a plus) and modern ML frameworks such as PyTorch, TensorFlow, scikit-learn .
• check_circle Experienced in fine-tuning, prompting, and evaluating LLMs and integrating them into production systems.
• check_circle Skilled in cloud deployment (AWS/GCP/Azure), containerisation (Docker/Kubernetes), and building scalable ML systems.
• check_circle Comfortable designing and managing end-to-end ML pipelines with tools like Airflow, Kedro, ZenML, dbt.
• check_circle Passionate about exploring emerging AI trends, open-source frameworks, and applying them to real-world challenges.
• check_circle Strong communicator, able to explain technical concepts to non-technical audiences, and thrive in collaborative, cross-disciplinary environments.
• check_circle Creative, curious, and driven with a proven ability to deliver results in fast-paced and high-growth settings.

Preferred qualifications

• Prior experience launching AI/ML products into production.
• Exposure to graph-based models, multi-agent AI systems, and generative models (LLMs, diffusion, etc.).
• Experience with data-driven decision making, A/B testing, and advanced evaluation strategies .
• Familiarity with AI coding assistants and rapid prototyping tools.
• Strong high-level programming skills (e.g., Python), frameworks and tools such as DeepSpeed, Pytorch lightning, kubeflow, TensorFlow, etc.
• Build features end-to-end using technologies like TypeScript, MongoDB, and Elasticsearch, and contribute to our compute orchestration layer powered by Temporal.
• Technical skills including familiarity with Python, GPU, AWS, API, LLM, ML, and SQL
• Experience across the stack: React, Typescript, Node, Python, etc.
• Hybrid role based in New York.
• Opportunity to shape cutting-edge AI products that reach millions of users.
• Exposure to cross-functional teams and access to strong career development resources.
• A fast-paced, innovative environment with the chance to influence technical strategy and best practices.