Generative AI Engineer

9 hours ago

Bangalore Division, India Recro Full time

🧠 Hiring: AI Architect (7+ yrs) — Agentic & GenAI Systems 📍 Location: Bengaluru / Gurugram | 💼 Full-time | AI Platforms & Architecture Team 🚀 About the Role We’re looking for an AI Architect with a strong engineering background and proven expertise in Agentic Frameworks and Generative AI platforms . You’ll own the design and scale of production-grade agentic systems , ensuring they’re secure, observable, and optimized for performance and cost on AWS. This role demands a hands-on architect — someone who can debug code, model infrastructure, and lead developers through deep technical challenges. ⚙️ What You’ll Do Architect and scale Agentic AI systems (planning, reasoning, and tool-calling) and GenAI/RAG pipelines . Design production architectures on AWS (EKS, Bedrock, S3, SQS/SNS) with a focus on cost, performance, and reliability. Implement and optimize multi-agent frameworks such as Semantic Kernel, LangGraph, AutoGen, or CrewAI . Build retrieval and hybrid search pipelines with grounding, caching, and hallucination control. Drive observability via OpenTelemetry + Datadog (metrics, tracing, SLIs/SLOs). Set security, compliance, and data-governance standards for GenAI deployments. Mentor engineers, conduct design reviews, and lead technical architecture discussions. 🎯 What We’re Looking For 7–10 years in software/AI engineering with at least 4+ years in GenAI applications . Agentic framework experience is mandatory (Semantic Kernel, LangGraph, AutoGen, or CrewAI). Expert in Python (3.11+), FastAPI, asyncio, profiling, and pytest . Proven experience in architecting GenAI/RAG systems using AWS Bedrock , OpenSearch Serverless, or equivalent. Deep understanding of AWS cloud services , distributed systems, and observability. Passion for building reliable, secure, and high-performing AI platforms at scale. 🌟 Bonus Points Expertise in multi-agent orchestration and reasoning flows. Hands-on experience with vector databases (Pinecone, Redis, pgvector). Familiarity with evaluation frameworks , rollout gates, and FinOps optimization. Exposure to policy-as-code (OPA) , feature management (LaunchDarkly) , or NeMo Guardrails .

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