Job
Description
About The Role
Project Role :Technology Architect
Project Role Description :Review and integrate all application requirements, including functional, security, integration, performance, quality and operations requirements. Review and integrate the technical architecture requirements. Provide input into final decisions regarding hardware, network products, system software and security.
Must have skills :Python (Programming Language)
Good to have skills :CommerceTools Commerce Platform
Minimum 3 year(s) of experience is required
Educational Qualification :15 years full time education
Summary:Design, build, and ship GenAI features end to end across web/backend stacks. Youll implement LLM powered experiences (chat, copilots, content automation), robust RAG pipelines, and API integrations using Python and modern front end frameworks, deploying on AWS/Azure/GCP.
Roles & Responsibilities:
Implement GenAI features (chatbot flows, agent tools, content generation, summarization) with Python backends (FastAPI/Flask/Django) and React/Angular front ends.Build RAG pipelines:ingestion, chunking, embeddings, vector search, retrieval orchestration, response templating.Integrate cloud AI services:AWS Bedrock (Claude, Titan), Azure OpenAI (GPT 4o family), Google Vertex AI (Gemini); plus model endpoints from Hugging Face.Develop data connectors to S3/Blob/GCS, Kendra/Azure AI Search/Vertex Search, relational/NoSQL stores.Engineer prompt templates, tool use, guardrails, and evaluation harnesses (toxicity, hallucinations, latency, quality).Implement observability & MLOps hooks (OpenTelemetry, logging, tracing, CI/CD), model/config versioning, blue/green deployments.Write secure, testable code (unit/integration tests), perform code reviews, and contribute to reusable libraries/components.
Professional & Technical Skills:Strong Python (async, typing), REST/GraphQL APIs, microservices; JavaScript/TypeScript for UI.LLMs & GenAI fundamentals:prompting, function/tool calling, structured outputs, evaluation.RAG:embeddings (Titan, text embedding ada/EP), vector DBs (Kendra/AI Search/OpenSearch/FAISS), retrieval strategies (hybrid).Cloud fluency:AWS:Bedrock, Lambda, S3, API Gateway, Step Functions, DynamoDB, Kendra, OpenSearch.Azure:OpenAI, Functions, Key Vault, Cosmos DB, Azure AI Search, App Service, AKS.GCP:Vertex AI, Cloud Run, Cloud Functions, BigQuery, Firestore.CI/CD (GitHub Actions/Azure DevOps), containers (Docker, Kubernetes), IaC (Terraform/CloudFormation/Bicep).Security:secret management (Key Vault/Secrets Manager), data privacy, prompt injection defenses.Experience with multi agent frameworks (LangGraph/LangChain), streaming (Server Sent Events/WebSockets).Front end design systems, accessibility, and performance tuning.Exposure to analytics/telemetry pipelines for model quality monitoring
Additional Information: Bachelors/Masters in CS/Engineering (or equivalent).Typically 710 years total experience; 24 years in applied GenAI/LLM solutions.
Qualification15 years full time education
