AI Engineer

***We are unable to sponsor or take over sponsorship of an employment visa at this time.*** **Job Title: Senior Gen AI / Agentic AI Engineer** **Role Overview** We are seeking a highly skilled **Senior Gen AI / Agentic AI Engineer** to design, build, and deploy enterprise-grade Generative AI and Agentic AI platforms. The role requires strong hands-on experience across **LLMs, RAG, Graph RAG, multi-agent orchestration, vector databases, MCP setup, full-stack application development, cloud-native deployments, observability, and data engineering pipelines**. The ideal candidate should be capable of building scalable AI platforms from end to end, including data ingestion, embedding pipelines, retrieval systems, agent workflows, model serving, API layers, UI/UX integration, monitoring, security, and production deployment across multi-cloud environments. **Key Responsibilities** - Design and develop **enterprise Gen AI and Agentic AI applications** using LLMs, RAG, Graph RAG, multi-agent workflows, and tool-augmented reasoning. - Build scalable **RAG pipelines** including document ingestion, chunking, embedding generation, metadata enrichment, hybrid search, reranking, retrieval optimization, and response grounding. - Implement **Graph RAG** solutions by integrating knowledge graphs, entity extraction, relationship mapping, graph traversal, and contextual retrieval. - Develop **multi-agent systems** using frameworks such as **LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, LlamaIndex**, and custom orchestration patterns. - Set up and integrate **MCP servers and clients** to enable tool connectivity, enterprise system integration, agent-to-tool communication, and reusable AI workflows. - Build and manage **vector database solutions** using Pinecone, Weaviate, Milvus, FAISS, Chroma, OpenSearch Vector Engine, Azure AI Search, Vertex AI Vector Search, or pgvector. - Deploy and optimize **LLM / VLLM inference stacks** using vLLM, Hugging Face Transformers, TensorRT-LLM, TGI, Ollama, llama.cpp, Ray Serve, or Triton Inference Server. - Integrate commercial and open-source LLMs such as **GPT, Claude, Gemini, Llama, Mistral, Mixtral, Falcon, Cohere, DeepSeek**, and domain-specific fine-tuned models. - Develop secure and scalable backend services using **Python, FastAPI, Flask, Node.js, Java/Spring Boot**, or similar API frameworks. - Build full-stack applications with frontend technologies such as **React, Angular, Next.js, TypeScript, JavaScript, HTML, CSS**, and integrate AI workflows into user-facing interfaces. - Create intuitive **UI/UX experiences** for AI chatbots, agent workbenches, document intelligence platforms, prompt playgrounds, feedback loops, approval workflows, and human-in-the-loop systems. - Implement **data engineering pipelines** using Spark, PySpark, Databricks, Airflow, Kafka, Snowflake, BigQuery, Redshift, SQL, NoSQL, and cloud-native data services. - Build ingestion pipelines for structured, semi-structured, and unstructured data including PDFs, Word documents, emails, images, logs, databases, APIs, and enterprise repositories. - Deploy AI workloads on **AWS, Azure, and GCP**, using services such as Bedrock, SageMaker, Azure OpenAI, Azure AI Search, Azure ML, Vertex AI, BigQuery, GKE, AKS, EKS, Lambda, and Cloud Functions. - Implement cloud-native architecture using **Docker, Kubernetes, Helm, Terraform, CI/CD, GitHub Actions, GitLab, Jenkins**, and infrastructure-as-code practices. - Establish strong **monitoring and observability** for Gen AI applications, including prompt/response tracing, token usage, latency, hallucination tracking, retrieval quality, cost monitoring, model drift, and agent execution traces. - Use tools such as **LangSmith, Arize Phoenix, W&B Weave, MLflow, Evidently AI, Prometheus, Grafana, OpenTelemetry, Splunk, Datadog, ELK**, and cloud-native logging platforms. - Implement **LLMOps / MLOps** practices including model registry, prompt versioning, evaluation pipelines, A/B testing, gu…