AI Engineer
New
IndiaFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 3–5 years
- Required Skills
- DockerPythonFlaskFastAPICI/CDRESTful APIsMicroservices
Requirements
- 3–5 years of experience as an AI Engineer or Python Backend Engineer working with production systems.
- Strong proficiency in Python with experience in production-grade backend development.
- Hands-on experience with Flask and familiarity with FastAPI, Uvicorn, and Gunicorn.
- Experience working with background job systems such as Dramatiq, Celery, or RQ.
- Practical experience with LLMs and VLMs, including prompt engineering, evaluation, and fine-tuning concepts.
- Experience integrating or working with LLM routing platforms such as OpenRouter or equivalent tools.
- Strong understanding of microservices architecture and scalable system design.
- Solid experience designing and maintaining REST APIs, including authentication and rate limiting.
- Familiarity with Docker, CI/CD pipelines, logging, monitoring, and production debugging.
- Experience building AI evaluation or feedback systems is highly desirable.
- Exposure to cloud platforms such as AWS or GCP for deployment and scaling.
- Prior experience with SaaS or AI-first product environments is a strong advantage.
Responsibilities
- Maintain and optimize LLM- and VLM-powered services for content generation, compliance scoring, and campaign testing workflows.
- Manage and scale Flask and FastAPI microservices, ensuring high availability, low latency, and system reliability.
- Design, maintain, and monitor Dramatiq (or equivalent) queues for asynchronous AI workflows and pipeline orchestration.
- Deploy and support Uvicorn/Gunicorn-based services in production, ensuring stability and efficient resource utilization.
- Integrate and manage OpenRouter or similar LLM routing tools to optimize cost, latency, and model quality trade-offs.
- Design prompt engineering strategies to improve accuracy, contextual awareness, and compliance of AI outputs.
- Build and maintain AI feedback loops, including human-in-the-loop evaluation and automated quality assessment systems.
- Develop and maintain secure, versioned REST APIs for AI services with proper authentication and rate limiting.
- Collaborate with backend and frontend teams to ensure a scalable and maintainable microservices architecture.
- Monitor production metrics such as token usage, latency, and error rates to ensure optimal system performance.
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