AI Engineer - Agentic

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 SUMMARY

We’re looking for a hands-on Agentic AI Engineer to build, optimize, and deploy production-grade AI solutions. In 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.

Role: Agentic AI Engineer

Experience: 2–5 years

Employment Type: Full-time

WHAT YOU'LL DO

Agentic Development

Build: Develop LLM-based applications and multi-step agentic workflows using frameworks such as Microsoft Agent Framework, AutoGen, LangChain, LangGraph, LlamaIndex, or CrewAI

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 via FastAPI endpoints; integrate with client data sources and third-party APIs

Vector Databases: Manage embeddings and retrieval using Pinecone, Qdrant, or pgvector

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 using LangSmith or Arize Phoenix, tracking accuracy, hallucinations, cost, and latency

CI/CD: Maintain CI/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; participate in peer code reviews and contribute to the team’s internal library of reusable AI patterns and playbooks

MUST-HAVE QUALIFICATIONS

Experience: 2–5 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: LangChain, LangGraph, CrewAI, AutoGen, or LlamaIndex

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 deploying, monitoring, and maintaining AI 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.Tech / B.E. / M.Sc. in Computer Science, Information Technology, or a related field

GOOD TO HAVE

Evaluation: Exposure to G-Eval, RAGAS, TruLens, or LangSmith for quantifying LLM output quality

MLOps: Basic experience with MLflow or DVC for experiment tracking

Structured Outputs: Experience with Pydantic-based output parsing and function/tool calling

Performance Tuning: Knowledge of vLLM or Triton Inference Server for high-throughput model serving

Certifications: AWS / Azure AI Fundamentals or equivalent cloud certification

Originally posted on Himalayas