AI Engineer – Agentic AI

Job Overview

We are looking for an AI Engineer with a strong foundation in Agentic AI and Natural Language Processing (NLP). The role will involve designing, developing, and deploying intelligent systems using LLMs, multi-agent frameworks, and autonomous agents to solve real-world problems.

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Roles & Responsibilities

Agentic AI & Multi-Agent Systems

  • Design and implement agent-based architectures using CrewAI, LangGraph, AutoGen, LangChain, or similar frameworks
  • Build multi-agent workflows for task orchestration, autonomous decision-making, and execution
  • Integrate LLMs with tools, APIs, and memory systems

Model Development & Fine-Tuning

  • Fine-tune and adapt pre-trained LLMs (e.g., Mistral Large, Gemini Flash, OpenAI models, AWS Bedrock)
  • Develop NLP solutions for summarization, classification, and text generation
  • Contribute to internal or open-source model repositories

Data Engineering & Processing

  • Pre-process and analyze large-scale datasets
  • Collaborate with data engineering teams on scalable data pipelines

Evaluation & Optimization

  • Implement evaluation metrics
  • Optimize models for performance, latency, and scalability

Collaboration & Research Integration

  • Partner with research, product, and engineering teams
  • Stay current with advancements in LLMs, agentic architectures, and AI safety

Documentation & Communication

  • Maintain technical documentation for models and agent workflows
  • Present findings to both technical and non-technical stakeholders
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Required Qualifications

  • Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related fields
  • Strong programming skills in Python
  • Experience with PyTorch / TensorFlow
  • Hands-on experience with LLMs, prompt engineering, and fine-tuning
  • Exposure to Agentic AI / multi-agent systems
  • Experience with distributed systems and large-scale data processing
  • Strong problem-solving and collaboration skills

Preferred Skills

  • Experience with WatsonX, LangChain, AutoGen, LangGraph
  • Cloud exposure: IBM Cloud, AWS, GCP, Azure
  • Open-source contributions
  • Experience with RAG and vector databases
  • Understanding of AI safety, alignment, and ethical AI

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