GenAI / Agentic AI Developer
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.
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
Pay
The gross annual starting base salary is 130,000–150,000 (full-time). This covers base pay only; any bonuses, incentives, and benefits will be discussed later in the recruitment process. Candidates with additional experience or qualifications may receive a higher offer, determined by objective, gender-neutral criteria and consistent with our pay principles. If a collective labour agreement applies, we will explain the relevant pay terms at the interview stage. Note: We never ask for your current or previous salary during our hiring process.
Location
New York, US