Experience:

• 5-7 years of hands-on experience in Python development

Qualification:

• Bachelor's or Master's degree in Computer Science, Engineering, AI/ML, or a related field

Additional responsibilities:

• collaborate with cross-functional teams to translate business requirements into technical deliverables
• mentor junior engineers and ensure adherence to coding and documentation standards

Nice to have:

• Experience with RAG frameworks such as LangChain, LlamaIndex, or Haystack
• Knowledge of additional AWS AI services (Textract, Comprehend, Translate, OpenSearch, Kendra)
• Familiarity with MLOps practices
• CI/CD pipelines (AWS CodePipeline, CodeBuild)
• containerization (Docker, Kubernetes)
• Experience with data processing libraries (Pandas, NumPy) and ML frameworks (TensorFlow, PyTorch, scikit-learn)
• Strong understanding of vector database optimization, scaling, and hybrid search techniques
• Knowledge of graph databases (Neo4j, Amazon Neptune) and knowledge graph integration with RAG systems
• Experience with cost optimization, A/B testing, and observability tools (CloudWatch, X-Ray, Prometheus)
• Understanding of AWS security best practices, including IAM, KMS, and VPC design
• Hands-on experience designing and implementing RAG architectures
• Proficiency with backend databases (PostgreSQL, MySQL, MongoDB, DynamoDB, etc.), including schema design and optimization
• Experience developing cloud-based solutions using AWS (SageMaker, Bedrock, Lambda, ECS/EKS, Step Functions)
• Practical experience with at least one vector database (Qdrant, Pinecone, Weaviate, Chroma, FAISS, Milvus, or pgvector)
• Hands-on experience integrating Large Language Models (GPT, Claude, Llama, or Titan) within RAG systems
• Proficiency in working with embedding models (OpenAI, Cohere, Sentence Transformers, or Amazon Titan)

More skills:

AI/ML, NLP, Retrieval-Augmented Generation (RAG) systems, vector databases (Qdrant, Pinecone, Weaviate, etc.), AWS AI/ML services (SageMaker, Bedrock, Kendra, Lambda, etc.), spaCy, React, Streamlit, Haystack, MLOps, CI/CD pipelines (AWS CodePipeline, CodeBuild), containerization (Docker, Kubernetes), data processing libraries (Pandas, NumPy), ML frameworks (TensorFlow, PyTorch, scikit-learn), graph databases (Neo4j, Amazon Neptune), knowledge graph integration with RAG systems, cost optimization, A/B testing, observability tools (CloudWatch, X-Ray, Prometheus), AWS security best practices, IAM, KMS, VPC design, backend databases (PostgreSQL, MySQL, MongoDB, DynamoDB, etc.), Large Language Models (GPT, Claude, Llama, or Titan), embedding models (OpenAI, Cohere, Sentence Transformers, or Amazon Titan)