Material+ - Lead AI Engineer

About Us :

We turn customer challenges into growth opportunities.

Material is a global strategy partner to the worlds most recognizable brands and innovative companies. Our people around the globe thrive by helping organizations design and deliver rewarding customer experiences.

We use deep human insights, design innovation and data to create experiences powered by modern technology. Our approaches speed engagement and growth for the companies we work with and transform relationships between businesses and the people they serve.

Srijan, a Material company, is a renowned global digital engineering firm with a reputation for solving complex technology problems using their deep technology expertise and leveraging strategic partnerships with top-tier technology partners. Be a part of an Awesome Tribe.

Role : Lead Agentic AI Engineer

Experience : 510 years

Employment Type : Full-time

Role Summary :

We are looking for a Lead Agentic AI Engineer who can own the end-to-end design and delivery of complex, production-grade agentic systems. You will be the go-to technical expert and the engine room of our most demanding AI initiatives turning ambiguous client challenges into scalable, functional platforms. You will drive technical solutioning for client engagements, architect multi-agent pipelines, and bridge AI engineering with business outcomes while elevating the capability of the team around you.

What You'll Do : 1. Agentic Architecture & Engineering :

• System Design : Architect multi-agent systems orchestrator/sub-agent patterns, state machines, tool registries using Microsoft Agent Framework, LangGraph, CrewAI, AutoGen, or Semantic Kernel.

• Advanced RAG : Design and optimize retrieval pipelines : hybrid search, re-ranking, query expansion, multi-hop reasoning, and knowledge graphs.

• Model Adaptation : Apply Quantization, PEFT/LoRA fine-tuning, and prompt optimization techniques to adapt foundation models for client-specific tasks.

• Guardrails & Hallucination Control : Design and enforce comprehensive guardrail frameworks output validation, factual grounding checks, prompt injection defenses, content filtering, and hallucination-mitigation strategies for enterprise-grade deployments.

2. MLOps & Production Readiness :

• Deployment : Productionize AI services on AWS / Azure using Docker, Kubernetes, and CI/CD pipelines (GitHub Actions / Azure DevOps).

• Observability : Build comprehensive monitoring for LLM systems tracking accuracy, hallucinations, latency, cost, and drift using LangSmith or Arize Phoenix.