LLM & Agentic AI Engineer (Mid-Level, 1–2 yrs)

4 days ago

Islamabad, Islamabad, Pakistan Rapids AI Full time 900,000 - 1,200,000 per year

Company Description:

Rapids AI
builds practical AI: custom LLMs, RAG search, multi-agent automation, voice and vision pipelines, and secure APIs. We turn real business workflows into measurable, deployable AI products.

Role Description:

Build, adapt, and ship LLM/RAG and agentic AI solutions for real-world workflows—owning model adaptation, multi-agent orchestration, evaluation/safety, and productionization.

Location
: NUST, Islamabad (On-site)
• Type: Full-time

Qualifications:

  • Experience:
    1–2 years in Python-based AI/ML; hands-on with LLMs/agents.
  • Skills:
    PyTorch or Transformers; LangChain/LangGraph; RAG (FAISS/Milvus/pgvector); REST (FastAPI/Flask); Git & testing; Docker.
  • Exposure (plus):
    SFT/PEFT (must); DPO/PPO (nice); vLLM/TGI, LoRA/QLoRA, ; eval/guardrails (RAGAS/HELM/custom); healthcare data standards (FHIR/HL7), de-identification/PHI handling; basic K8s, W&B/MLflow.

Key Responsibilities:

  • Adapt LLMs to diverse use-cases using
    SFT, PEFT, DPO, PPO
    and strong prompting/programmatic prompting.
  • Optimize
    RAG
    (chunking, embeddings, retrieval, grounding, latency/cost) to improve decision-support and information access.
  • Design, develop, and deploy
    agentic AI systems
    for multi-step workflows with human-in-the-loop oversight.
  • Build/orchestrate
    multi-agent architectures
    with
    LangChain, LangGraph
    (or similar), including routing, tools, guards, and monitoring.
  • Implement
    agent memory, planning, and tool-calling
    to interact with external APIs, databases, and enterprise systems.
  • Develop
    feedback loops and evaluation frameworks
    for reliability, safety, and alignment with organizational policies.
  • Collect, clean, and refine datasets
    for training/adaptation of LLMs and agents.
  • Convert/package models for
    production
    (quantization, containers, inference servers) and maintain high code quality.

Stack You'll Touch:

Python
• PyTorch
• Hugging Face
• LangChain/LangGraph
• FastAPI
• FAISS/Milvus/pgvector
• Postgres/Redis
• Docker
• vLLM/TGI

How to Apply?

Send your
CV + GitHub
to
,
along with a short cover letter on why you're the right fit.

If you're applying via email, please include answers to the questions below.

  1. What is your current salary in PKR?
  2. What is your notice period in days?
  3. Can you join immediately?
  4. How many hours of US time-zone overlap can you provide per weekday?
  5. How many years of hands-on LLM/agent experience do you have?
  6. Have you built an end-to-end RAG pipeline? (Yes/No)
  7. Have you implemented a tool-calling agent? (Yes/No)
  8. Have you performed SFT/PEFT fine-tuning? (Yes/No)
  9. Have you used DPO or PPO for preference optimization? (Yes/No)
  10. How many total years of professional experience do you have?

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