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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and deploying artificial intelligence solutions, particularly in creating conversational flows and AI agents using LLMs and generative AI architectures. Proficient in managing the machine learning model lifecycle and integrating web APIs within cloud platforms.
Highest-signal resume keywords
Python ProficiencyAWS SageMaker ExperienceLangChain KnowledgeAPI-Based IntegrationNoSQL Database Management
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Artificial Intelligence SolutionsConversational FlowsChatbot DevelopmentPrompt EngineeringWeb API DevelopmentSQL ProficiencyRAG ArchitecturesVector Search ImplementationMachine Learning Model LifecycleCode Version Control
Tools & Technologies
AWS OpenSearchAWS DocumentDBFastAPIFlaskLangFlowN8nAWS BedrockVertex AIAzure OpenAILangGraph
Industry Keywords
Generative AILLMOpsAI AgentsData-Driven Decision-MakingOptical Character Recognition
Tech Stack
Tools & technologiesAWSAzureCloudFlaskNoSQLPythonSQL
About the role
Key responsibilities & impact- Design, develop, and deploy artificial intelligence solutions at scale
- Create conversational flows and AI agents
- Develop solutions using LLMs, LLMOps, and generative AI architectures
- Deliver robust, secure applications integrated with the organization’s technology ecosystem
- Create chains, agents, and memory flows using LangChain and LangGraph
- Deploy and manage the machine learning model lifecycle using AWS SageMaker
- Implement vector search and RAG architectures using AWS OpenSearch
- Manage metadata and history storage in a NoSQL database using AWS DocumentDB
- Develop and integrate web APIs using FastAPI or Flask
- Perform code version control using Git
- Apply Prompt Engineering practices
- Work with frameworks such as LangChain, LangFlow, n8n, or similar tools
- Work with cloud platforms such as AWS Bedrock (Agentcore), Vertex AI, and Azure OpenAI
- Apply LLMOps practices and API-based integration to build intelligent agents
- Support data-driven decision-making aligned with the client’s business objectives
Requirements
What you’ll need- Proven experience developing chatbots, creating conversational flows, and building AI agents
- Proficiency in Python
- Excellent knowledge of SQL
- Strong knowledge of LangChain and LangGraph
- Hands-on experience with AWS SageMaker
- Hands-on experience with AWS OpenSearch for vector search and RAG architectures
- Hands-on experience with AWS DocumentDB for storing metadata and history in NoSQL
- Knowledge of developing web APIs using FastAPI or Flask
- Knowledge of code version control using Git
- Solid understanding of RAG architectures
- Knowledge of Prompt Engineering
- Knowledge of LLMs, LLMOps, and generative AI architectures
- Experience with frameworks such as LangChain, LangFlow, n8n, or similar tools
- Experience with cloud platforms such as AWS Bedrock (Agentcore), Vertex AI, and Azure OpenAI
- Experience with API-based integration for developing intelligent agents
- Experience with OCR (Optical Character Recognition) processes for extracting and processing text from documents and PDFs is a plus
Benefits
Comp & perks- Remote work
- Professional development and personal growth opportunities
- Proprietary and third-party AI-powered tools
- Borderless talent environment
- Exposure to market trends and disruptive AI technologies