AI & ML Engineer enhancing energy management software solutions at GreenPocket GmbH. Focusing on modern LLM architectures and AI integration for innovative user experience.
About the role
- AI/ML Engineer driving AI transformation at Nimonik by designing and implementing innovative AI solutions. Collaborating with teams to leverage regulatory databases and enhance software capabilities.
Responsibilities
- Collaborate with various teams to define business requirements
- Serve as a technical center of excellence for AI and ML initiatives
- Design, develop, and deploy AI solutions in production using traditional ML models, RAG + LLMs, fine-tuned LLMs, and agentic systems
- Identify data collection and annotation needs, and ensure datasets are properly prepared for training and fine-tuning models
- Evaluate and benchmark AI models using appropriate performance metrics for accuracy, efficiency, and business relevance
- Collaborate with software engineers to integrate AI applications into Nimonik’s platform.
- Stay up to date with the latest advancements in AI, experiment and apply them
- Document code, methodologies, and experiments, and contribute to the knowledge sharing within the team
- Participate in SR&ED reporting.
Requirements
- 1+ years of relevant experience in AI software development and machine learning engineering, including experience with:
- Cloud platforms (preferably, AWS and its services like Amazon Bedrock, SageMaker, Lambda, OpenSearch etc.)
- SQL (e.g., PostgreSQL)
- API development (FastAPI, Flask, etc.)
- ML tools such as PyTorch, Tensorflow, HuggingFace, etc.
- Training, fine-tuning, evaluating, and deploying BERT and similar transformer-based models
- Open source and proprietary LLMs such as Claude, GPTs, and Qwen
- Open source and proprietary vector databases such as OpenSearch, Weaviate and Pinecone
- Implementation of RAG pipelines using LangChain or similar frameworks
- MCP and agentic LLM systems
- Deployment of AI applications with a focus on data privacy and security.
- Excellent skills in Python software development
- Ability to think out of the box, design AI solutions based on the newest technology, and deliver the results.
Benefits
- Health insurance
- Three weeks’ paid vacation to start
- Paid Fridays off in July
- Paid extended Christmas break
- Family-friendly policies
- Great office coffee
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
Machine Learning Engineer responsible for implementing and maintaining data science models in bpx’s machine learning studio. Bridging data science and computational needs to achieve business outcomes.
Machine Learning Engineer at DentalMonitoring developing AI solutions for orthodontics. Responsibilities include model development, evaluation, deployment, and performance monitoring.
Machine Learning Engineer at Hiscox working on fraud detection and generative AI projects. Collaborating closely with data scientists and engineers to solve complex business challenges.
Internship focusing on programming robotic arms and using machine learning in simulations at Fraunhofer IIS. Opportunity to gain practical experience and contribute to innovative research.
Senior Machine Learning Engineer at Itaú, driving innovation with data and AI solutions. Collaborating across teams to implement robust machine learning architectures and ensure scalable deployments.
Machine Learning Engineer responsible for developing and deploying advanced ML and AI solutions at Zendesk. Collaborating with stakeholders to deliver impactful business outcomes using latest machine learning technologies.
Lead advanced machine learning model development and optimization at PayPal. Collaborate with teams to deploy scalable ML solutions in production environments.
Senior Machine Learning Engineer at Pivotal Health developing ML systems for healthcare reimbursement. Collaborating across teams to build and maintain reliable, production - grade machine learning systems.
Machine Learning Engineer working with Algorithm team on customer onboarding processes. Focus on execution and automation of models using computer vision and AI in sports industry.