AI Engineer

Senior Gen AI / Agentic AI Engineer

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
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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.

• 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 Lang Chain, Lang Graph, CrewAI, Auto Gen, Semantic Kernel, Llama Index, 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, Open Search 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, Deep Seek, 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, Type Script, 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, Big Query, Redshift, SQL, No
  
  SQL, 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, Sage Maker, Azure OpenAI, Azure AI Search, Azure ML, Vertex AI, Big Query, GKE, AKS, EKS, Lambda, and Cloud Functions.
• Implement cloud‑native architecture using Docker, Kubernetes, Helm, Terraform, CI/CD, Git Hub Actions, Git Lab, 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 Lang Smith, Arize Phoenix, W&B Weave, MLflow, Evidently AI, Prometheus, Grafana, Open Telemetry, Splunk, Datadog, ELK, and cloud‑native logging platforms.
• Implement LLMOps / MLOps practices including model registry, prompt versioning, evaluation pipelines, A/B testing, guardrails, feedback capture, safety checks, and automated deployment.
• Apply security and governance controls including PII detection, data masking, access control, RBAC, IAM, encryption, audit logging, policy enforcement, prompt injection prevention, and responsible AI guardrails.
• Collaborate with product owners, architects, data scientists, engineers, UX teams, security teams, and business stakeholders to deliver…