Qualification
We are looking for a Software Engineer who combines deep data engineering expertise with hands-on experience in Generative AI and Agentic AI system development on AWS Cloud.
This role is ideal for someone who can design, build, and deploy production-grade GenAI workflows integrating LLMs, vector databases, and orchestration frameworkswith the same rigor as a traditional data system.
Required Skills & Experience
- 610 years of total experience in Data Engineering with strong AWS background.
- Proficiency in Pyspark with hands-on production grade experience.
- Hands-on experience with GenAI solutions in real-world environments (not just demos or PoCs).
- Working knowledge of Agentic AI frameworks (LangChain, LlamaIndex, AutoGen, or similar).
- Good hands-on experience in Python
- Cloud experience is must have, AWS is preferred.
- Experience with RAG architecture, vector databases (OpenSearch, Pinecone, FAISS, Chroma, or Milvus), and embedding models.
- Understanding of LLMOps, prompt lifecycle management, and performance monitoring.
- Practical experience deploying workloads on AWS ECS/EKS, setting up CI/CD pipelines, and managing runtime performance.
- Familiarity with IAM, VPC, Secrets Manager, and security best practices in cloud environments.
Nice To Have
- Experience with AWS Bedrock for model hosting or SageMaker for fine-tuning and evaluation.
- Exposure to multi-agent architectures and autonomous task orchestration.
- Contributions to open-source GenAI projects or internal AI platform initiatives.
Role
Key Responsibilities
- Design and maintain data pipelines and AI data infrastructure on AWS (Glue, Lambda, S3, Redshift, Step Functions, Athena, etc.).
- Develop and deploy LLM-based applications and Agentic AI workflows using frameworks like LangChain, LlamaIndex, or AutoGen.
- Build RAG (Retrieval-Augmented Generation) pipelines using AWS services (S3 + Bedrock + SageMaker + OpenSearch/Vector DB).
- Implement agentic reasoning, tool calling, and orchestration for multi-agent workflows.
- Containerize and deploy AI services using Docker, ECS, or EKS, ensuring scalability, cost-efficiency, and observability.
- Integrate AWS Bedrock, SageMaker, or OpenAI APIs with internal data systems and applications.
- Set up monitoring, tracing, and model observability using AWS CloudWatch, X-Ray, or third-party LLMOps tools.
- Collaborate with ML engineers, data scientists, and architects to take GenAI prototypes to production-ready deployments.
Experience
7 to 12 years
Job Reference Number
13295
