AWS Bedrock & Generative AI

Design and build GenAI solutions using AWS Bedrock and foundation models (FM) such as Amazon Titan, Anthropic Claude, etc.

Implement LLM-based applications like chatbots, summarization engines, recommendation systems, and document intelligence

Apply advanced prompt engineering, fine-tuning, and model evaluation techniques

Design RAG (Retrieval-Augmented Generation) pipelines and integrate vector databases

Python Development

Develop scalable backend systems using Python (FastAPI, Flask, Django)

Build APIs and microservices to integrate AI/ML services into business applications

Write efficient scripts for data preprocessing, automation, and orchestration

AWS Cloud & Architecture

Design cloud-native solutions using AWS services (Lambda, API Gateway, S3, EC2, IAM, CloudWatch, Step Functions)

Architect secure, scalable, and highly available systems

Optimize cloud cost and performance

Ensure data privacy and compliance for AI workloads

Data & AI Integration

Work with structured & unstructured datasets for AI applications

Build pipelines to integrate external data sources with LLMs

Implement vector search, embeddings, and semantic search capabilities

Collaboration & Leadership

Collaborate with data scientists, ML engineers, product teams, and business stakeholders

Lead design discussions and provide technical guidance to junior developers

Participate in Agile/Scrum development cycles

• Primary skills:Technology->Geographical Information System->Spatial Databases->SQL Server,Technology->OpenSystem->Python - OpenSystem

Preferred Skills

Experience with LangChain, LlamaIndex, or similar frameworks

Knowledge of vector databases (FAISS, Pinecone, Weaviate, OpenSearch)

Exposure to Docker, Kubernetes, and containerization

Understanding of ML lifecycle, MLOps, and model deployment

Experience in big data or data engineering tools (Glue, Redshift, Spark)