About the Role We are hiring a GenAI and Agentic AI Application Engineer to design, build, deploy, and operate secure, scalable enterprise AI applications across AWS and GCP. This is an application-engineering role focused on LLM integration, Retrieval-Augmented Generation (RAG), AI agents, local model hosting, security, evaluation, and observability. Experience in banking, financial services, or another regulated industry is preferred. What You Will Do · Build enterprise copilots, knowledge assistants, conversational applications, document intelligence solutions, and AI-enabled workflows. · Design production-grade RAG pipelines covering ingestion, chunking, embeddings, vector and hybrid search, reranking, grounding, citations, and access-aware retrieval. · Develop single-agent and multi-agent solutions with tool calling, workflow orchestration, memory, identity propagation, human approvals, and controlled execution boundaries. · Integrate foundation models through Amazon Bedrock, Google Vertex AI, approved model APIs, and locally hosted open-source models. · Create secure APIs, microservices, asynchronous and event-driven workflows, streaming responses, structured outputs, prompt management, guardrails, and fallback mechanisms. · Implement GenAI and agent evaluation for groundedness, relevance, hallucination, citation accuracy, safety, task completion, tool-selection accuracy, latency, and cost. · Establish end-to-end observability for prompts, retrieval, model calls, agent actions, tool calls, token usage, errors, performance, and infrastructure consumption. · Apply CI/CD, automated testing, infrastructure as code, prompt and agent versioning, controlled releases, rollback, and production support practices. Required Experience · 3+ years of software or application engineering experience,



including hands-on delivery of GenAI applications to production. · Strong Python skills and experience with FastAPI, Flask, Django, or equivalent backend frameworks. · Practical expertise in LLMs, prompt engineering, embeddings, vector databases, RAG, structured outputs, context management, tool calling, and AI agents. · Experience with agent or orchestration frameworks such as Amazon Bedrock Agents, LangGraph, LangChain, LlamaIndex, Semantic Kernel, or equivalent. · Experience building REST APIs, microservices, asynchronous services, event-driven applications, and enterprise integrations. · Working knowledge of Docker, Git, Linux, automated testing, CI/CD, and infrastructure as code. · Strong understanding of application security, API security, IAM, encryption, privacy controls, and secure handling of sensitive enterprise data. AWS Skills: Hands-on Experience Expected · Amazon Bedrock: foundation models, Agents, Knowledge Bases, Guardrails, and evaluation. · Amazon SageMaker: hosting and managing custom or open-source foundation models. · S3, Lambda, EC2 and GPU instances, ECS/EKS, API Gateway, DynamoDB, RDS/Aurora PostgreSQL, and OpenSearch. · Step Functions, SQS, SNS, and EventBridge for workflow orchestration and asynchronous processing. · CloudWatch, CloudTrail, and X-Ray for monitoring, audit, logging, and tracing. · IAM, KMS, Secrets Manager, VPC, and PrivateLink for identity, encryption,



secrets, and network isolation. · AWS CDK, CloudFormation, or Terraform for infrastructure as code. GCP Experience Experience with relevant GCP services, including Vertex AI, Gemini, Model Garden, Vertex AI Agent Builder, Vertex AI Search, Cloud Storage, Cloud Run, GKE, Pub/Sub, Workflows, BigQuery, Cloud SQL or AlloyDB, Cloud Logging and Monitoring, IAM, Secret Manager, Cloud KMS, and VPC Service Controls. Robust AWS expertise is required; practical GCP experience or demonstrated ability to build cloud-portable GenAI applications is expected. Security, Local Hosting & GenAIOps · Protect applications against prompt injection, jailbreaks, sensitive-data leakage, unauthorized retrieval, malicious documents, insecure tool use, and excessive agent autonomy. · Implement least privilege, encryption, private networking, data masking or redaction, access-aware retrieval, tool allowlists, validation, rate limits, human approvals, and audit trails. · Host approved open-source models using SageMaker, EC2 GPU, ECS/EKS, Vertex AI, GKE, or controlled private infrastructure; experience with vLLM, Hugging Face TGI, NVIDIA Triton, or ONNX Runtime is desirable. · Optimize latency, throughput, concurrency, GPU utilization, context usage, token consumption, reliability, and cost; implement autoscaling, health checks, load testing, rollback, and disaster recovery. Preferred · Experience with multimodal AI, document AI, OCR, intelligent document processing, hybrid search, knowledge graphs, or reranking. · Knowledge of responsible AI, AI governance, model risk, GenAI threat modelling, OWASP guidance for LLM applications, and regulated-industry controls. · Relevant AWS or GCP certification and ex

📌 GenAI & Agentic AI Application Engineer (Bengaluru)
🏢 HIRESTAR JOB BANK
📍 Bengaluru