Engineering Lead Microsoft Data & AI - L1

Wipro•Dallas, TX

23h

About The Position

As a Senior Engineer or Architect on the Agentic System Layer (ASL) team, you will define and drive the technical architecture for an agentic AI platform. The role includes designing multi-agent orchestration systems, tool-use frameworks, and LLM integration pipelines, and establishing production-scale patterns for agent reliability, observability, and guardrails. You will lead technical design reviews, create architecture decision records, collaborate with ML and software engineers, evaluate and integrate agentic AI frameworks, define API contracts and data-flow patterns, and mentor engineers in production-grade AI development. The role also covers requirements gathering and documentation, solution design for assigned modules, code review and configuration, testing and user acceptance, root-cause analysis, implementation and integration support, preparation of user-training materials, maintenance of project documentation and reports, stakeholder collaboration, and improving testing and programming practices through automation and other innovative methods. The stated compensation range is $60,000 to $135,000. Final compensation depends on factors including location, applicable minimum-wage obligations, skills, and relevant experience. The role is eligible for Wipro's standard benefits, including medical and dental options, disability insurance, paid time off (inclusive of sick leave), and other paid and unpaid leave options. Employment in some roles may be conditioned on passing a post-offer drug screening, subject to applicable state law.

Requirements

10+ years of software architecture experience, including at least 3 years designing AI/ML platform systems.
Hands-on experience with LLM orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, or similar.
Deep expertise in distributed systems design, microservices architecture, event-driven patterns, and REST/gRPC API design.
Strong proficiency in Python and at least one of Java, Go, or TypeScript.
Production experience building and deploying agentic AI systems or LLM-powered applications at scale.
Experience with prompt engineering, tool-use patterns, RAG pipelines, and agent reliability and observability.
Mandatory skill: Agentic AI.
The posting separately states experience of 5–8 years.

Nice To Haves

Experience with Kubernetes or container orchestration.
Experience with AWS, Azure, or GCP cloud platforms.
Experience with MLOps/LLMOps tooling.
Experience with vector databases such as Pinecone, Weaviate, or pgvector.
Experience with knowledge graphs.
Airline or travel domain experience.
TOGAF or similar architecture certification.
Experience with multi-agent system design patterns and agent evaluation or benchmarking frameworks.

Responsibilities

Design and evolve architecture for multi-agent orchestration systems, tool-use frameworks, and LLM integration pipelines.
Establish production-scale patterns for agent reliability, observability, and guardrails.
Lead technical design reviews and produce architecture decision records (ADRs).
Collaborate with ML and software engineers to ensure platform components are scalable, secure, and maintainable.
Evaluate and integrate agentic AI frameworks.
Define API contracts, data-flow patterns, and integration standards across the AI platform ecosystem.
Mentor engineers on best practices for building production-grade AI systems.
Gather requirements for assigned modules and document them clearly and structurally.
Capture cross-process linkages, dependencies, and required changes.
Facilitate application solution designs for assigned modules under Project Manager guidance, aligned with business requirements and technical standards.
Review developed code, resolve team technical queries, and create consistent replication patterns aligned with project standards.
Collaborate with project managers throughout the software development life cycle.
Monitor and implement testing activities for assigned modules and conduct user acceptance testing (UAT).
Perform root-cause analysis to troubleshoot recurring technical issues.
Modify software code to resolve errors, adapt to new hardware and software, improve performance, and upgrade interfaces.
Collaborate with technical teams to validate configurations, integrate new applications, and address deployment dependencies.
Prepare user-training documents and frameworks to support adoption and minimize transition risks.
Maintain technical documents, flowcharts, layouts, and diagrams in alignment with project objectives.
Compile and submit project specifications, progress updates, resource requirements, and status reports for Project Manager review.
Collaborate with project managers, client stakeholders, and technical teams to gather and refine requirements and define technical expectations.
Gather stakeholder feedback, align deliverables, evaluate software-hardware interactions, and define performance specifications.
Develop improved testing and programming practices using innovative methods to increase automation and deliver error-free code.