About US:-
We turn customer challenges into growth opportunities.
Material is a global strategy partner to the world’s 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
Employment Type: Full-time
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.
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
Guardrails & Hallucination Control: Design and enforce comprehensive guardrail frameworks — output validation, factual grounding checks, prompt injection defenses, content filtering, and hallucination-mitigation strategies (chain-of-verification, retrieval grounding, self-consistency) — for enterprise-grade deployments
MLOps & Production Readiness
Observability: Build comprehensive monitoring for LLM systems — tracking accuracy, hallucinations, latency, cost, and drift using LangSmith or Arize Phoenix
Evaluation: Define and implement LLM evaluation suites using RAGAS, G-Eval, TruLens, or custom metrics aligned to client KPIs
Cost & Token Optimization: Drive down inference costs through token budgeting, prompt compression, KV-cache management, model routing, streaming strategies, and intelligent batching — balancing performance against cost at scale
CI/CD: Own and evolve CI/CD pipelines for ML systems, enforcing automated testing (unit, contract, and model-quality tests) as a standard across all engagements
Client Solutioning & Leadership
Mentorship: Guide junior engineers, review architecture decisions, and build the team’s internal library of reusable AI patterns, accelerators, and playbooks
Agentic Frameworks: Proven experience building production agents with LangGraph, CrewAI, AutoGen, or Semantic Kernel
RAG & Retrieval: Deep expertise in RAG architectures, vector databases (Pinecone, Qdrant, Weaviate), and embedding pipelines
LLMs: Strong working knowledge of GPT-4o, Claude 3.x/4.x, Gemini, and open-source models (Llama 3, Mistral)
Engineering: Strong Python, FastAPI, SQL; software design patterns; a “software engineering first” approach to ML — with rigorous unit, integration, and model-quality testing
MLOps & Productionization: Proven track record taking LLM systems from prototype to production — owning deployment pipelines, observability, evaluation suites, guardrails, and ongoing model health in live client environments
