AI Engineer LLM & Agent Design

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Role Overview

Senior AI Engineer LLM Agent

agentic AI systems

Key Roles & Responsibilities

LLM Agent & AI System Development

• Design and build

  LLM-based agents

  with tool/function calling and conversation state management.
• Implement

  multi-turn dialogue handling

  and optimize

  context window utilization

  .
• Develop agent workflows with

  planning, reasoning, and decision-making capabilities

  .
• Build

  short-term and long-term memory schemas

  for conversational and task-based agents.
• Optimize memory retrieval strategies to ensure

  context relevance and accuracy

  .

Retrieval-Augmented Generation (RAG)

• Design and implement

  RAG pipelines

  with efficient document chunking strategies.
• Integrate

  vector stores

  and build scalable retrieval pipelines.
• Implement retrieval strategies and intelligent context injection mechanisms.
  
• Work with embeddings, similarity search, and relevance optimization.
  

Backend & Platform Engineering

• Develop

  robust, scalable Python services

  for AI workloads.
• Build and expose

  REST / GraphQL APIs

  for LLM-based services.
• Design systems using

  microservices architecture

  .
• Integrate AI services with

  cloud platforms and data infrastructure

  .
• Implement logging, monitoring, error handling, and system observability.
  

Safety, Evaluation & Reliability

• Implement

  guardrails and safety mechanisms

  for LLM outputs.
• Instrument systems for

  evaluation, monitoring, and performance tracking

  .
• Build prompt templates, orchestration logic, and fallback strategies.
  
• Ensure production readiness, scalability, and fault tolerance.
  

You Might Be Our Ideal Match If You:

Core Experience

• Have

  3–5 years

  of experience in

  software engineering or ML engineering

  .
• Bring

  1–2 years of hands-on experience

  building

  LLM-based systems or agents

  .
• Are

  proficient in Python

  (mandatory).

LLM & Agent Expertise

• Have experience with

  LLM agent frameworks

  such as

  LangChain, LlamaIndex, or DSPy

  .
• Are skilled in

  prompt engineering

  , prompt templates, and orchestration logic.
• Have hands-on experience with

  RAG implementations

  and guardrails.
• Understand

  memory management

  , context windows, and conversation state.

Vector Databases & Retrieval

• Have experience with vector stores such as

  FAISS, Pinecone, or Weaviate

  .
• Understand

  embeddings

  , similarity search, and vector indexing fundamentals.

APIs, Cloud & DevOps

• Have experience building and integrating

  REST / GraphQL APIs

  .
• Are familiar with

  microservices

  and cloud deployment on

  AWS, Azure, or GCP

  .
• Use

  Git

  and follow version control best practices.

Good to Have

• Experience integrating with

  OpenAI, Azure OpenAI, Claude, or Meta LLMs

  .
• Hands-on experience with

  local model deployment

  (Llama, Mistral).
• Understanding of

  MLOps basics

  including

  Docker, CI/CD pipelines

  .
• Familiarity with

  model/version management and observability

  (logs, metrics, traces).

Education

• BE / BTech

  in Computer Science, Information Technology, or equivalent practical experience.