Lead Full Stack AI Engineer
Role Overview
At least 8 years of JavaScript experience and 8 years of Python experience are required.
Experience with high-traffic portals or sites involving real-time analytics, OTT, or live channels is required.
The role reports to a team of developers and focuses on leading generative AI engineering.
Key Skills for This Role
Full Job Posting
Location and work arrangement
- The role is described as work from home/remote, with Gurugram listed as a location preference.
Experience and role
At least 8 years of JavaScript experience and 8 years of Python experience are required.
Experience with high-traffic portals or sites involving real-time analytics, OTT, or live channels is required.
The role reports to a team of developers and focuses on leading generative AI engineering.
Responsibilities
- Drive generative AI vision, strategy, and architecture; build and improve AI applications and their underlying systems.
- Develop backend systems and APIs for AI products and agent frameworks, and integrate LLMs into scalable production systems.
- Create reusable model-building, deployment, and monitoring frameworks with monitoring, logging, tracing, and alerting.
- Build platform guardrails, compliance rules, and oversight workflows for model updates and staged production releases.
- Create templates, guides, and sandboxes for onboarding and experimentation.
- Require validation testing and peer review for user-facing applications and promote good software engineering practices.
Expertise
- Experience leading AI projects from conception to deployment in a consultancy or startup environment; professional software engineering experience, ideally in backend, APIs, or AI products.
- Proficiency in Python/Django and JavaScript/TypeScript/React; strong understanding of backend architecture, REST or GraphQL APIs, and data modeling.
- Experience with Docker and cloud platforms such as GCP, Azure, or similar; practical experience with SQL or NoSQL databases.
- Strong software design and system design foundation; leadership and a track record of mentoring and developing talent.
- Strong communication with nontechnical stakeholders and a strategic, problem-solving approach.
- Expertise spanning statistics, analytics, big data, data science, machine learning, deep learning, cloud, mobile, and full-stack technologies; hands-on large-data analysis experience.
- Exposure to agentic frameworks such as LangChain, LangGraph, or Semantic Kernel is an advantage.
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