Princ Engr-AI Science
Hybrid · Hyderabad, Telangana, India or Chennai, Tamil Nadu, India
Job Summary
Principal Full Stack AI Engineer to architect, develop, and deploy end-to-end AI-powered commercial applications. Leads full-stack development of AI applications, ensuring seamless integration between AI models (LLMs, predictive models) and web interfaces. Designs and deploys scalable APIs and microservices to serve ML models; develops intuitive front-end interfaces (React/Next.js) and robust back-end architectures with PostgreSQL, MongoDB, and vector databases. Emphasizes privacy, ethical modeling, and bias-free implementations; drives DevOps and CI/CD for production deployments; mentors talent across data science and software engineering; advocates for infrastructure-as-code and containerization (Docker, Kubernetes) across on-premise, cloud, and edge environments; collaborates with academia and startups to advance AI tooling and practices. Required background includes a bachelor’s degree or 6+ years of experience, experience delivering production AI solutions, and strong proficiency across Python/FastAPI, Node.js, Go, React/Next.js, TypeScript, PyTorch/TensorFlow, LangChain/LlamaIndex/OpenAI APIs, Kafka/Spark, AWS/GCP, Docker/Kubernetes.
Required Qualifications
- Bachelor’s degree or six or more years of work experience
- Experience independently developing algorithms, writing production-grade code, and deploying complex software/AI solutions to production
- Experience scaling AI in commercial applications (e.g., Generative AI, LLMs, Personalization, Computer Vision) and integrating them into full-stack applications
- Full Stack Development: Python/FastAPI, Node.js, Go
- Frontend frameworks: React, Next.js, TypeScript/JavaScript, TailwindCSS
- AI/ML & Data Engineering: PyTorch, TensorFlow, Scikit-learn, Hugging Face
- Modern AI stacks: LangChain, LlamaIndex, OpenAI APIs, Vector Databases
- Data pipelines: Kafka, Spark
- Cloud platforms: AWS, GCP
- Container orchestration: Docker, Kubernetes
- Core concepts: data structures, data modeling, modern software architecture patterns
- Collaborative and communicative skills
- Ability to explain complex code and math to non-technical stakeholders
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