Principal Scientist - AI Research

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 strong

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