GenAI Developer

Role: GenAI DeveloperLocation: NYC NY.Job Type: Full time Note: 10+ Year required (Only USC AND GC)Job Description:We are looking for a hands-on GenAI / Agentic AI Developer to build LLM-powered applications, RAG solutions, and agentic AI workflows for enterprise use cases.Key ResponsibilitiesBuild GenAI applications using LLMs, RAG, agents, and tool-calling workflows.Develop agentic solutions using LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, or LlamaIndex.Design and implement multi-agent workflows such as planner, retriever, executor, validator, and human-in-the-loop agents.Build backend APIs using Python, FastAPI, Flask, REST APIs, and microservices.Integrate AI agents with enterprise systems, databases, APIs, document repositories, and cloud services.Implement document ingestion, embeddings, vector search, reranking, and retrieval pipelines.Deploy and monitor GenAI applications using Docker, Kubernetes, CI/CD, and cloud platforms.Support LLMOps including prompt/version management, model evaluation, monitoring, logging, and cost tracking.Required SkillsStrong hands-on experience in Python development.Experience with OpenAI, Azure OpenAI, AWS Bedrock, Anthropic Claude, Gemini, Llama, or Mistral.Hands-on experience with at least one agentic framework: LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, or LlamaIndex.Good understanding of RAG, embeddings, vector databases, semantic search, and prompt engineering.Experience with vector stores such as OpenSearch, Pinecone, FAISS, Chroma, Weaviate, Milvus, Azure AI Search, or pgvector.Knowledge of REST APIs, cloud deployment, Docker, CI/CD, and software engineering best practices.Ability to work with structured and unstructured data including PDFs, documents, APIs, databases, and knowledge bases.Preferred SkillsExperience with multi-agent orchestration, tool calling, memory, planning, reflection, and evaluation.Exposure to MCP, Graph RAG, Neo4j, knowledge graphs, or entity extraction.Knowledge of LLMOps tools such as LangSmith, MLflow, Phoenix, Ragas, TruLens, Arize, or OpenTelemetry.Experience with AWS Bedrock/SageMaker, Azure OpenAI/AI Search, or GCP Vertex AI.Understanding of AI guardrails, prompt injection prevention, PII masking, access control, and responsible AI.Must-HaveCandidate should be able to clearly explain at least one end-to-end GenAI / Agentic AI project, including problem statement, architecture, tools used, deployment approach, evaluation method, and business impact."