AI Engineer

About the role

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.

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.
• 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.
• 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.
• 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.
• 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.
• Implement LLMOps / MLOps practices including model registry, prompt versioning, evaluation pipelines, A/B testing, guardrails, feedback capture, safety checks, and automated deployment.
• Collaborate with product owners, architects, data scientists, engineers, UX teams, security teams, and business stakeholders to deliver production-grade AI solutions.
• Optimize Gen AI applications for accuracy, latency, scalability, reliability, cost, and user experience.

Requirements

• Strong hands-on experience in Generative AI, LLMs, Agentic AI, RAG, Graph RAG, and prompt engineering.
• Experience building multi-agent AI systems using LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar frameworks.
• Strong understanding of LLM orchestration, tool calling, function calling, agent memory, planning, reasoning, task decomposition, and workflow automation.
• Hands-on experience with RAG architecture, including chunking strategies, embeddings, vector search, hybrid search, reranking, metadata filtering, and evaluation.
• Experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, Chroma, Azure AI Search, OpenSearch, Vertex AI Vector Search, or pgvector.
• Knowledge of Graph RAG / knowledge graph solutions using Neo4j, Neptune, TigerGraph, RDF, SPARQL, Cypher, or graph-based retrieval patterns.
• Full-stack development experience with React, Angular, Next.js, TypeScript, JavaScript, HTML, CSS, and API integration.
• Experience with model serving and inference optimization using vLLM, Hugging Face, TGI, Triton, TensorRT-LLM, Ray Serve, or similar platforms.
• Strong cloud experience across AWS, Azure, and/or GCP.
• Hands-on experience with Docker, Kubernetes, Helm, Terraform, CI/CD pipelines, and cloud-native deployments.
• Strong data engineering skills using SQL, Python, Spark/PySpark, Databricks, Airflow, Kafka, and cloud data platforms.
• Experience implementing observability, monitoring, logging, tracing, and evaluation for AI/ML/LLM applications.
• Strong understanding of LLMOps/MLOps, model lifecycle management, prompt lifecycle, evaluation metrics, and production support.
• Experience with security, governance, responsible AI, guardrails, prompt injection protection, and enterprise compliance controls.

Nice to have

• Hands-on experience setting up MCP server/client architecture for enterprise agentic AI platforms.
• Experience with A2A / agent-to-agent communication, multi-agent collaboration, and tool registry patterns.
• Experience with fine-tuning, LoRA, QLoRA, PEFT, RLHF, RLAIF, or domain-specific model adaptation.
• Experience with document intelligence platforms, OCR, extraction pipelines, and intelligent search.
• Experience with Databricks Mosaic AI, Azure AI Foundry, AWS Bedrock Agents, Google Vertex AI Agent Builder, or similar enterprise AI platforms.
• Experience with semantic caching, token optimization, prompt compression, and cost optimization.
• Familiarity with Neo4j, AWS Neptune, OpenSearch, Elasticsearch, Redis, MongoDB, PostgreSQL, Snowflake, BigQuery.
• Experience in regulated industries such as banking, financial services, healthcare, or insurance.