Principal Scientist - AI Research

As the AI Systems Architect, you’ll 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.
Public
 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

Work arrangement
Hybrid