Lead AI Engineer I - R01571043

Analyst

Job requirements

Experience Range: With at least 2 to 4 years of experience in full-stack data science, including practical exposure to cloud-native architectures and agentic AI frameworks Key Responsibilities:

Design and develop end-to-end full-stack applications, covering frontend, backend, and API components

Build and deploy agentic AI applications using multi-agent systems and autonomous workflows

Implement scalable backend systems with microservices and event-driven architectures to support intelligent solutions

Develop cloud-native solutions on platforms such as Azure, AWS, and GCP, ensuring robust and scalable deployments

Integrate LLM-based frameworks and agent orchestration tools to enable adaptive and intelligent workflows

Enforce best practices in code quality, testing, debugging, observability, performance optimization, security, and scalability

Collaborate with cross-functional teams to deliver technical solutions aligned with business requirements

Contribute to design reviews and architectural decisions for AI-driven systems

Required Skills:

Full-stack development with React, Angular, or Vue for frontend

Backend development using Node.js, Java Spring Boot, or Python frameworks (FastAPI, Django)

RESTful API design and microservices architecture

Experience with Azure, AWS, and GCP cloud platforms

Hands-on experience with Docker containers and Kubernetes

CI/CD pipeline implementation

Agentic AI frameworks such as LangChain, LlamaIndex, Semantic Kernel, AutoGen, or CrewAI

Prompt engineering and Retrieval-Augmented Generation (RAG) techniques

Preferred Skills:

Familiarity with vector databases such as FAISS or Pinecone

Knowledge of event streaming systems like Kafka or Pub/Sub

Experience with federated learning or privacy-preserving algorithms

Contributions to open-source projects or hackathons in AI/ML or full-stack domains

Experience with model experimentation platforms such as Domino Datalabs or Databricks ML

Desired Qualifications:

Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, or a closely related discipline

Certification in cloud platforms (e.g., AWS Certified Machine Learning Specialty, Azure AI Engineer Associate, Google Cloud Professional Machine Learning Engineer)

Certification in full-stack development or AI frameworks (e.g., Full Stack Web Development, TensorFlow Developer Certificate)

Additional Information: Location: Bangalore (Hybrid work arrangement)