Principal Engineer

Wipro; Bengaluru; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Wipro is seeking a Principal Scientist - AI Research to own end-to-end design and delivery of production-grade agentic and Generative AI systems in Bengaluru. The role requires deep architectural insight, coding proficiency, and leadership across product, data, and SRE teams.

You will architect secure, cost-efficient AI platforms on AWS and guide developers through complex optimization while ensuring observability and reliability.

Qualifications

• Education: Bachelor's/Master's from IIT/Tier-1 in Computer Science, AI, or related field.
• 7-10 years in software/AI engineering, incl. 4+ years GenAI app 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.
• Deep understanding of AWS ecosystem: EKS, Bedrock, S3, SQS/SNS, RDS, ElastiCache, Secrets Manager, IAM/Okta, Kong API Gateway.

Responsibilities

• Architect Production AI Systems: create robust architectures for agentic systems and GenAI/RAG pipelines.
• Optimize for Cost & Performance: tune model throughput, latency, and multi-node GPU clusters.
• Lead Debugging & Stability: deep-dive debugging, fix defects, and ensure high availability.
• Standardize Agentic Frameworks: build reference implementations using Semantic Kernel, LangGraph, AutoGen, or CrewAI.
• Implement Observability & Monitoring: setup tracing, metrics, and logging with OpenTelemetry and Datadog.
• Govern Evaluation & Rollouts: build test/evaluation frameworks and controlled rollouts.
• Establish Engineering Standards: reusable SDKs, connectors, CI/CD templates, review checklists.
• Cross-Functional Leadership: collaborate with product, data, and SRE, mentor engineers.

Skills

Python 3.11+

GenAI application development

Agentic AI systems

MLOps/stability practices

Distributed training & multi-node GPU

Cross-functional collaboration

Education

Bachelor's/Master's in CS/AI or related (IIT/Tier-1)

Tools

Semantic Kernel

LangGraph

AutoGen

CrewAI

AWS Bedrock

OpenSearch Serverless

Pinecone

Redis

Job description

Job description: Principal Engineer | Title: Principal Scientist - AI ResearchAs 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.
• 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.