Senior AI Engineer

Job Title: Senior AI Engineer

Location: Ankara

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.