Work Authorization

Candidates must be authorized to work in the United States without current or future employer sponsorship.

Visa sponsorship is not available for this position. Candidates requiring sponsorship now or in the future, including H-1B, OPT, CPT, F-1, TN, E-3, L-1, or similar employment-based sponsorship, are not eligible for consideration.

Job Description

We are seeking a highly skilled and hands-on GenAI / Agentic AI Developer to design, build, and deploy enterprise-grade AI solutions powered by Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and multi-agent architectures.

The ideal candidate will have strong Python development expertise and practical experience implementing GenAI applications, agent orchestration frameworks, vector search technologies, and cloud-native AI solutions. This role requires someone who can move from proof-of-concept to production while ensuring scalability, reliability, security, and business value.

Key Responsibilities

• Design and develop GenAI solutions using LLMs, RAG, tool calling, and agent-based architectures.
• Build and orchestrate multi-agent workflows, including planner, retriever, executor, validator, and human-in-the-loop patterns.
• Develop backend services and APIs using Python, FastAPI, Flask, REST APIs, and microservices.
• Design and implement document ingestion, embedding generation, vector indexing, reranking, and retrieval pipelines.
• Integrate AI applications with enterprise systems, APIs, databases, document repositories, and cloud services.
• Deploy, monitor, and support GenAI applications using Docker, Kubernetes, CI/CD pipelines, and cloud platforms.
• Implement LLMOps best practices, including model evaluation, prompt management, monitoring, logging, observability, and cost optimization.
• Collaborate with business and technology stakeholders to deliver scalable AI solutions that generate measurable business outcomes.

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field, or equivalent professional experience.
• 5+ years of hands-on Python development experience.
• Experience building and deploying GenAI or Agentic AI applications in enterprise environments.
• Hands-on experience with one or more of the following:
• • LangGraph
  • LangChain
  • AutoGen
  • CrewAI
  • Semantic Kernel
  • LlamaIndex
• Strong understanding of:
• • Retrieval-Augmented Generation (RAG)
  • Embeddings
  • Prompt Engineering
  • Semantic Search
  • Vector Databases
• Experience working with one or more of the following:
• • OpenAI
  • Azure OpenAI
  • AWS Bedrock
  • Anthropic Claude
  • Gemini
  • Llama
  • Mistral
• Experience with vector platforms such as:
• • OpenSearch
  • Pinecone
  • Chroma
  • FAISS
  • Weaviate
  • Milvus
  • Azure AI Search
  • pgvector
• Experience developing REST APIs and cloud-native applications.
• Knowledge of Docker, Kubernetes, CI/CD, and software engineering best practices.
• Experience working with structured and unstructured data sources, including documents, PDFs, APIs, databases, and knowledge repositories.

Preferred Qualifications

• Experience designing and deploying multi-agent AI systems.
• Experience with tool calling, memory management, autonomous planning, reflection, and evaluation techniques.
• Exposure to:
• • MCP (Model Context Protocol)
  • GraphRAG
  • Neo4j
  • Knowledge Graphs
  • Entity Extraction
• Experience with LLMOps tools such as:
• • LangSmith
  • MLflow
  • Phoenix
  • Ragas
  • TruLens
  • Arize
  • OpenTelemetry
• Experience with:
• • Azure AI Foundry
  • Azure OpenAI
  • Azure AI Search
  • AWS Bedrock
  • AWS SageMaker
  • GCP Vertex AI
• Knowledge of AI governance, responsible AI, AI guardrails, prompt injection prevention, PII masking, and access controls.

Required Candidate Experience

Candidates must be able to clearly explain at least one end-to-end GenAI or Agentic AI implementation, including:

• Business problem being solved
• Overall solution architecture
• LLMs and frameworks leveraged
• Agent orchestration approach
• RAG and vector search design
• Deployment strategy
• Evaluation and monitoring methodology
• Business impact and measurable outcomes