AI Engineer Agentic

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 SUMMARY

Werelooking for a hands-on Agentic AI Engineer to buildoptimize and deploy production-grade AI this role you will be the engine room of our AI initiatives taking architectural blueprints and turning them into scalable functional systems. You will work across the full build lifecycle from prototyping to production collaborating with Senior Architects and data scientists to deliver AI solutions in a consulting environment.

WHAT YOULL DO

Agentic Development

• Build:Develop LLM-based applications and multi-step agentic workflows using frameworks such as Microsoft Agent FrameworkAutoGenLangChainLangGraphLlamaIndex orCrewAI

• RAG Pipelines:Implement Retrieval-Augmented Generation pipelines: chunking embedding vector search and re-ranking

• Tool Use & Memory:Build agents with tool-calling short/long-term memory and human-in-the-loop checkpoints

• Prompt Engineering:Design and iterate on system prompts chain-of-thought templates and structured output schemas

• Model Fine-tuning:Execute fine-tuning and optimization tasks (Quantization PEFT/LoRA) to adapt foundation models for specific domain tasks

Integration & Delivery

• APIs:Expose AI capabilities viaFastAPIendpoints; integrate with client data sources and third-party APIs

• Vector Databases:Manage embeddings and retrieval using PineconeQdrant orpgvector

MLOps&Productionization

• Deployment:Containerize and deploy AI services using Docker and Kubernetes on AWS / Azure cloud environments ensuring high availability and low latency

• Observability:Implement observability for AI systems usingLangSmithorArizePhoenix tracking accuracy hallucinations cost and latency

• CI/CD:MaintainCI/CD pipelines for ML ensuring automated testing (unit contract and model-quality tests) is integrated into the delivery workflow

• Guardrails & Hallucination Control:Apply output validation guardrails and hallucination-detection techniques to ensure reliable production-safe AI outputs

• Token Optimization:Apply prompt compression context window management and response caching to control inference cost and latency

Collaboration

• Client Delivery:Work closely with Senior Architects and Engagement Managers to translate business requirements into technical tasks and working solutions

• Code Quality:Write clean modular Python;participatein peer code reviews and contribute to the teams internal library of reusable AI patterns and playbooks

MUST-HAVE QUALIFICATIONS

• Experience:25 years in software engineering data engineering or ML; at least 1 year building LLM/Gen AI applications

• Python:Strong Python skills OOP async programming packaging and testing

• LLM Frameworks:Hands-on experience with at least one of:LangChainLangGraphCrewAIAutoGen orLlamaIndex

• Gen AI:Working knowledge of LLM APIs (OpenAI Anthropic Claude Gemini) and prompt design

• Cloud Basics:Familiarity with AWS or Azure; comfortable with REST APIs and Git-based workflows

• MLOps&Productionization:Hands-on experience deployingmonitoring andmaintainingAI systems in production Docker/Kubernetes CI/CD pipelines and observability tooling are non-negotiable

• Engineering Fundamentals:Solid SQL skills API design experience (FastAPI/Flask) and a software engineering first approach to ML encompassing testing modularity and documentation

• Education:/ B.E. / . in Computer Science Information Technology or a related field

GOOD TO HAVE

• Evaluation:Exposure to G-Eval RAGASTruLens orLangSmithfor quantifying LLM output quality

• MLOps:Basic experience withMLflowor DVC for experiment tracking

• Structured Outputs:Experience withPydantic-based output parsing and function/tool calling

• Performance Tuning:Knowledge ofvLLMor Triton Inference Server for high-throughput model serving

• Certifications:AWS / Azure AI Fundamentals or equivalent cloud certification