Senior Full Stack GenAI Engineer with 10+ years of experience to design and build agentic AI solutions that automate enterprise workloads and business processes.The ideal candidate will have strong expertise in Python-based backend development, LLM-powered applications, cloud-native deployment, vector databases, and modern DevOps practices. This role involves building end-to-end AI systems that integrate with enterprise platforms, automate workflows, and deliver production-grade AI applications.Key Responsibilities• Design and develop agentic AI applications that automate enterprise workflows and decision-making processes.• Build scalable backend services using Python, FastAPI, and Pydantic.• Develop and deploy LLM-powered applications using models such as GPT and Claude.• Build AI agents and orchestration workflows using LangChain or Strands.• Implement Retrieval Augmented Generation (RAG) solutions using vector databases (pgvector, Pinecone, Weaviate).• Perform data analysis, preparation, and curation to build high-quality datasets for AI and knowledge retrieval systems.• Design and implement document ingestion pipelines for enterprise knowledge sources such as SharePoint, Confluence, and Jira.• Deploy AI workloads on AWS (Bedrock, ECS Fargate, S3) with proper security and scalability practices.• Develop and integrate enterprise APIs using REST, GraphQL, WebSockets, and web services.• Implement secure authentication and authorization using Ping Identity, OAuth2, OIDC, and SSO.• Build user interfaces for AI applications using ReactJS or Streamlit.DevOps & Deployment• Build and manage CI/CD pipelines using Jenkins or GitLab.• Implement GitOps practices for automated deployments.• Containerize applications using Docker and deploy to cloud platforms.• Implement security best practices, vulnerability scanning, dependency management, and container security.Required SkillsBackend & APIs• Python• FastAPI• Pydantic• REST APIs, GraphQL, WebSocketsGenAI & Agent Frameworks• LLMs (GPT, Claude)• LangChain or Strands• Retrieval Augmented Generation (RAG)• NLP (Natural Language Processing)Data & AI Pipelines• Data analysis, data preparation, and data curation• Document ingestion and knowledge base creation• Embeddings and semantic searchVector Databases• pgvector• Pinecone• WeaviateCloud & Platforms• AWS (Bedrock, ECS Fargate, S3, Guardrails)Databases• PostgreSQL• DynamoDBSecurity & Identity• Ping Identity• OAuth2 / OIDC• SSO, Authentication & AuthorizationDevOps• Jenkins• GitLab• GitOps practices• Docker containerization• Security vulnerability mitigationFrontend• ReactJS• StreamlitEnterprise Tools• Portkey (AI Gateway)• Apigee (API Gateway)• Jira, Confluence, SharePoint________________________________________Preferred Qualifications• Experience building AI agents for enterprise automation.• Experience implementing AI guardrails and LLM governance frameworks.• Experience building enterprise copilots or knowledge assistants.• Familiarity with LLMOps and AI observability platforms.________________________________________What We're Looking For• Strong full stack engineering mindset with GenAI expertise.• Experience building production-grade AI systems.• Ability to work across AI, backend, cloud, and DevOps stacks.• Passion for building automation solutions powered by agentic AI.________________________________________Experience• 10+ years of software engineering experience• 3+ years hands-on experience delivering GenAI-based enterprise applications
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Senior Full Stack GenAI Engineer with 10+ years of experience to design and build agentic AI solutions that automate enterprise workloads and business processes.The ideal candidate will have strong expertise in Python-based backend development, LLM-powered applications, cloud-native deployment, vector databases, and modern DevOps practices. This role involves building end-to-end AI systems that integrate with enterprise platforms, automate workflows, and deliver production-grade AI applications.Key Responsibilities• Design and develop agentic AI applications that automate enterprise workflows and decision-making processes.• Build scalable backend services using Python, FastAPI, and Pydantic.• Develop and deploy LLM-powered applications using models such as GPT and Claude.• Build AI agents and orchestration workflows using LangChain or Strands.• Implement Retrieval Augmented Generation (RAG) solutions using vector databases (pgvector, Pinecone, Weaviate).• Perform data analysis, preparation, and curation to build high-quality datasets for AI and knowledge retrieval systems.• Design and implement document ingestion pipelines for enterprise knowledge sources such as SharePoint, Confluence, and Jira.• Deploy AI workloads on AWS (Bedrock, ECS Fargate, S3) with proper security and scalability practices.• Develop and integrate enterprise APIs using REST, GraphQL, WebSockets, and web services.• Implement secure authentication and authorization using Ping Identity, OAuth2, OIDC, and SSO.• Build user interfaces for AI applications using ReactJS or Streamlit.DevOps & Deployment• Build and manage CI/CD pipelines using Jenkins or GitLab.• Implement GitOps practices for automated deployments.• Containerize applications using Docker and deploy to cloud platforms.• Implement security best practices, vulnerability scanning, dependency management, and container security.Required SkillsBackend & APIs• Python• FastAPI• Pydantic• REST APIs, GraphQL, WebSocketsGenAI & Agent Frameworks• LLMs (GPT, Claude)• LangChain or Strands• Retrieval Augmented Generation (RAG)• NLP (Natural Language Processing)Data & AI Pipelines• Data analysis, data preparation, and data curation• Document ingestion and knowledge base creation• Embeddings and semantic searchVector Databases• pgvector• Pinecone• WeaviateCloud & Platforms• AWS (Bedrock, ECS Fargate, S3, Guardrails)Databases• PostgreSQL• DynamoDBSecurity & Identity• Ping Identity• OAuth2 / OIDC• SSO, Authentication & AuthorizationDevOps• Jenkins• GitLab• GitOps practices• Docker containerization• Security vulnerability mitigationFrontend• ReactJS• StreamlitEnterprise Tools• Portkey (AI Gateway)• Apigee (API Gateway)• Jira, Confluence, SharePoint________________________________________Preferred Qualifications• Experience building AI agents for enterprise automation.• Experience implementing AI guardrails and LLM governance frameworks.• Experience building enterprise copilots or knowledge assistants.• Familiarity with LLMOps and AI observability platforms.________________________________________What We're Looking For• Strong full stack engineering mindset with GenAI expertise.• Experience building production-grade AI systems.• Ability to work across AI, backend, cloud, and DevOps stacks.• Passion for building automation solutions powered by agentic AI.________________________________________Experience• 10+ years of software engineering experience• 3+ years hands-on experience delivering GenAI-based enterprise applications
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