Gen AI

Key Responsibilities
Design develop and deploy GenAI applications using Google Gemini or Amazon Bedrock Vertex AI or SageMaker
Engineer and optimize prompts, pipelines, and LLM responses for scalable solutions
Integrate GenAI capabilities into business applications via REST APIs, AWS Lambda, and cloud-native services
Build and manage data pipelines using PostgreSQL for structured data access
Implement Retrieval-Augmented Generation (RAG) architectures and vector search workflows
Collaborate with cross-functional teams (ML engineers, product managers, IT, data teams) to gather requirements and deliver AI features
Ensure reliability, scalability, and performance of deployed AI models
Uphold 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 IAM
Experience working with Google Gemini or Amazon Bedrock, Anthropic Claude, Meta LLaMA, or other LLMs
Proficiency in Python and ML/NLP libraries Transformers, LangChain, spaCy
Strong knowledge of PostgreSQL for data querying and integration
Familiarity with vector databases (e.g., FAISS, Pinecone, Amazon Kendra) and semantic search
Understanding of prompt engineering, tokenization, and model fine-tuning
Experience building REST APIs and integrating GenAI into applications
Solid 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 frameworks
Background in NLP deep learning or conversational AI
Awareness of data privacy security and AI ethics principles