Ai / Ml Engineer

Company Description

Klugsys is a German technology company, founded by visionary technologists and business strategists, is dedicated to empowering enterprises through intelligent automation and immersive digital solutions. Beyond development, we act as partners in transformation, focusing on measurable impact, ethical innovation, and collaboration. Our commitment is to drive meaningful change across technological frontiers by working closely with our clients. Join us to be part of a team that prioritizes innovation and real-world results.

Role Description

As an AI / ML Engineer at Klugsys, you will design, build, and optimize AI models that power multiple AI features and multi-agent workflows.

You will work closely with our backend, cloud, and product teams to build scalable AI systems from model development to inference pipelines and LLM integrations. This role is best suited for someone who has strong foundations in both classical ML and modern LLM-based architectures.

Key Responsibilities

• Develop and maintain ML models for price prediction, lead engagement, and recommendation systems.
• Build AI workflows using LLMs and multi-agent frameworks (OpenAI, Bedrock, LangChain, custom agents).
• Fine-tune small LLMs or vision-language models for tasks like extraction, classification, or summarization.
• Build embeddings-based search (Qdrant / Pinecone / AWS Kendra) for property and insights data.
• Design and deploy inference pipelines on AWS using Lambda, ECS, or serverless GPU services.
• Build data pipelines for training datasets using AWS Glue, S3, Athena , and Pandas / PySpark.
• Work with multimodal features text, images, PDF extraction, photos, voice transcriptions.
• Implement prompt engineering, model evaluation, and continuous improvement cycles.
• Collaborate with backend teams to expose ML models as APIs for our mobile & web apps.
• Integrate with real-world data sources and historical transaction data.
• Monitor model performance, drift, and accuracy using automated evaluation dashboards.
• Maintain documentation for training data, model configurations, and inference outputs.
• Participate in architecture discussions for agentic AI workflows and model selection.

Required Skills & Experience

Core Technical Requirements (Must Have)

Bonus Skills (Good to Have)

Soft Skills

What we Offer