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Senior AI Engineer

3 weeks ago

Urla İlçesi, İzmir, Turkey Efsora Full time

Job Title:
Senior AI Engineer

Location:
Urla, Izmir

About Efsora:

We are a fast-growing software and AI development company that partners with innovative enterprises and scaleups to deliver large, cutting-edge R&D projects. Our teams work as extensions of our clients' internal R&D, combining advanced technology expertise to build impactful solutions. We focus on augmenting our clients' R&D capabilities, accelerating innovation, and managing technical risks from early-stage prototyping to full-scale deployment.

About the Role:

We are seeking a highly experienced Senior AI Engineer with 5+ years of experience to lead the development of multi-modal systems. This role requires a balanced command of LLMs and Computer Vision, enabling you to build agentic architectures that can not only "reason" but also "see," and interpret the world. You will play a pivotal role in designing, developing, and deploying goal-oriented AI solutions. You will move beyond text-only interfaces to build agents that process visual inputs to automate complex problem-solving and drive autonomous operational efficiency.

Responsibilities:

Multi-Modal Architecture & Agents

  • Lead the design and implementation of multi-modal agentic architectures that leverage LLMs as reasoning engines alongside specialized Computer Vision models.
  • Develop robust orchestration layers that enable agents to ingest video streams or images, interact with external APIs, and execute complex actions based on multi-sensory synthesis.
  • Design strategies for agents that utilize multi-step reasoning involving semantic understanding (text) and visual recognition (objects, OCR).

Engineering & Optimization

  • Design, develop, and maintain backend services and APIs that power multi-modal applications, ensuring scalability and performance for high-throughput media processing.
  • Implement specialized techniques for prompt engineering and computer vision pipeline optimization to ensure accurate context management and model performance.
  • Fine-tune LLMs and domain-specific Computer Vision models on custom datasets.

Deployment & Evaluation

  • Deploy and maintain sophisticated AI systems in production, ensuring reliability, cost optimization (e.g., managing token and GPU usage), and safety.
  • Develop evaluation metrics for multi-modal agents, focusing on task completion rates, accuracy across modalities, efficiency, and robustness.
  • Collaborate with product managers to translate complex requirements into practical, autonomous AI solutions and lead technical discussions on system architecture.

What We're Looking For:

Experience & Core Skills

  • Professional Experience:
    5+ years of professional experience in developing and deploying AI/ML models.
  • Programming:
    Expert proficiency in Python and deep familiarity with libraries such as PyTorch, TensorFlow/Keras, and OpenCV.
  • Problem-Solving:
    Exceptional ability to architect and debug complex, interdependent AI systems that span multiple modalities.

Balanced Domain Expertise (LLM & Vision)

  • Computer Vision Mastery:
    Deep experience with CNNs, Vision Transformers (ViTs), and standard tasks like Object Detection (YOLO, Faster R-CNN), Segmentation, and OCR.
  • LLM Proficiency:
    Extensive experience with Large Language Models, including fine-tuning via Hugging Face Transformers and utilizing advanced APIs (OpenAI, Anthropic, Gemini).
  • Multi-Modal Integration:
    Practical experience integrating structured outputs from Computer Vision models into LLM contexts to ground text generation in visual inputs.
  • Agentic Design:
    Proven track record in designing agentic architectures, specifically involving planning loops, tool use, and memory management.

Bonus Points If You Have:

  • Voice AI & Audio:
    Experience with ASR (Automatic Speech Recognition) and TTS (Text-to-Speech) technologies (e.g., OpenAI Whisper, ElevenLabs) for building voice-enabled conversational agents.
  • Orchestration Frameworks:
    Direct experience with agent orchestration frameworks (e.g., LangGraph, LlamaIndex, AutoGen).
  • Video Analytics:
    Experience applying AI agents to real-time video feeds or CCTV data.
  • MLOps & Cloud:
    Experience deploying complex, stateful systems on AWS, Azure, or GCP using Docker and Kubernetes.
  • Model Context Protocol:
    Familiarity with MCP.
  • Prototyping:
    Experience with Streamlit or Gradio for rapid multi-modal demos.