Gen AI

Key ResponsibilitiesDesign develop and deploy GenAI applications using Google Gemini or Amazon Bedrock Vertex AI or SageMakerEngineer and optimize prompts, pipelines, and LLM responses for scalable solutionsIntegrate GenAI capabilities into business applications via REST APIs, AWS Lambda, and cloud-native servicesBuild and manage data pipelines using PostgreSQL for structured data accessImplement Retrieval-Augmented Generation (RAG) architectures and vector search workflowsCollaborate with cross-functional teams (ML engineers, product managers, IT, data teams) to gather requirements and deliver AI featuresEnsure reliability, scalability, and performance of deployed AI modelsUphold secure, compliant, and ethical AI development practices

Required Skills & Qualifications

Hands-on experience with Google Cloud Platform (GCP) services: Vertex AI, Cloud Functions, BigQuery, IAM or experience with Amazon Web Services (AWS) including Amazon Bedrock, SageMaker, Lambda, and IAMExperience working with Google Gemini or Amazon Bedrock, Anthropic Claude, Meta LLaMA, or other LLMsProficiency in Python and ML/NLP libraries Transformers, LangChain, spaCyStrong knowledge of PostgreSQL for data querying and integrationFamiliarity with vector databases (e.g., FAISS, Pinecone, Amazon Kendra) and semantic searchUnderstanding of prompt engineering, tokenization, and model fine-tuningExperience building REST APIs and integrating GenAI into applicationsSolid grasp of CI/CD practices and cloud-based deployment

Preferred Qualifications

GCP or AWS Certifications (e.g., Associate Cloud Engineer, Professional ML Engineer, AWS Certified Machine Learning Specialty)Experience with LangChain RAG pipelines, or similar frameworksBackground in NLP deep learning or conversational AIAwareness of data privacy security and AI ethics principles