As the AI Systems Architect, youll own the end-to-end design and delivery of production-grade agentic and Generative AI systems. This is a highly hands-on role requiring deep architectural insight, coding proficiency, and an obsession with performance, scalability, and reliability. You’ll architect secure, cost-efficient AI platforms on AWS, guide developers through complex debugging and optimization,

and ensure all systems are observable, governed, and production-ready.

Key Responsibilities

- Architect Production AI Systems: Design robust overall architectures for agentic systems (planning, reasoning, tool-calling), GenAI/RAG pipelines, and evaluation workflows. Create detailed design documents including flow/UML/sequence diagrams and AWS deployment topologies. Additionally,

ensure architectures support advanced LLM training and inference workflows,

incorporating distributed strategies for scalability.

- Optimize for Cost & Performance: Model throughput, latency, concurrency,

autoscaling, CPU/GPU sizing, and vector index performance to ensure scalable, efficient deployments. Include optimization for multi-node GPU clusters and distributed training efficiency to reduce compute overhead.
- Lead Debugging & Stability Efforts: Conduct deep-dive debugging, fix critical defects, and resolve production incidents; pair-program with developers to improve code quality and performance. Apply MLOps-driven stability practices, leveraging configuration management and automated recovery for high availability.
- Standardize Agentic Frameworks: Build reference implementations using





Semantic Kernel (preferred), LangGraph, AutoGen, or CrewAI with solid schema validation, grounding, and memory management.
- Implement Observability & Monitoring: Set up distributed tracing, metrics,

and logging via OpenTelemetry and Datadog. Standardize dashboards, alerts,

and incident response workflows.

- Govern Evaluation & Rollouts: Build test and evaluation frameworks—golden sets, A/B experiments, regression suites, and controlled rollouts—to ensure consistent quality across releases.
- Establish Engineering Standards: Create reusable SDKs, connectors,

CI/CD templates, and architecture review checklists to promote consistency across teams.
- Cross-Functional Leadership: Collaborate with product, data, and SRE teams for capacity planning, DR strategies, and post-incident RCA reviews.

Mentor engineers to strengthen design and reliability practices

Required Qualifications

- Education: Bachelor’s/Master’s from a top-tier institute (IIT/Tier-1) in

Computer Science, AI, or related field.
- 7–10 years in software/AI engineering, including 4+ years in GenAI application development and 2+ years architecting agentic AI systems.
- Expert in Python 3.11+ (asyncio, typing, packaging, profiling, pytest).
- Hands-on experience with Semantic Kernel, LangGraph, AutoGen, or

CrewAI.
- Proven delivery of GenAI/RAG systems on AWS Bedrock or equivalent vector-based platforms (OpenSearch Serverless, Pinecone, Redis).
- Deep understanding of AWS ecosystem: EKS, Bedrock, S3, SQS/SNS,

RDS, ElastiCache, Secrets Manager, IAM/Okta, Kong API Gateway

📌 Principal Scientist - AI Research (Bengaluru)
🏢 Technoworkz Technologies
📍 Bengaluru