AI / ML Engineer
siemens
Job Description
- Key Responsibilities
- Design, develop, and deploy GenAI-powered applications using modern LLM ecosystems.
- Build AI agents and multi-agent systems to solve real business problems.
- Develop Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models.
- Integrate open-source and commercial LLMs through APIs and self-hosted deployments.
- Create scalable backend services and APIs for AI applications.
- Build intuitive frontend interfaces for AI-powered applications and dashboards.
- Implement prompt engineering, agent memory, tool calling, and workflow orchestration.
- Fine-tune, evaluate, and optimize AI/ML models for performance and cost efficiency.
- Collaborate with architects, product teams, and DevOps engineers to deliver production-grade AI solutions.
- Establish CI/CD pipelines, monitoring, observability, and deployment automation for AI systems.
- Stay updated with emerging trends in GenAI, agentic frameworks, multimodal AI, and autonomous systems.
- Computer Vision Applications: Work on AI-driven computer vision projects, including but not limited to image classification, object detection, segmentation, and image enhancement. Implement innovative computer vision algorithms for solving practical problems.
- AI & ML Algorithms: Leverage machine learning (ML) techniques to improve and fine-tune generative AI models and computer vision systems. Familiarity with both supervised and unsupervised learning techniques is essential.
- Research and Development: Stay up to date with the latest advancements in Generative AI, Computer Vision, and related fields. Experiment with new techniques and frameworks to continually improve the quality and capabilities of our solutions.
- Collaboration: Work closely with data scientists, software engineers, and product teams to deliver high-quality AI-based products. Contribute to the team’s technical discussions and knowledge sharing.
- Model Deployment & Maintenance: Work on deploying and maintaining AI models, ensuring reliability and performance. Set up and manage model training and inference pipelines.
- Qualifications:
- Education: bachelor’s or master’s degree in computer science, mathematics, or a comparable field with a focus on artificial intelligence
- Experience: Minimum of 3 to 4 years of hands-on experience in AI/ML development or data science, with a strong focus on Generative AI and Computer Vision. Experience working with open-source, locally hosted models is highly preferred.
- Technical Skills:
- Proficiency in deep learning frameworks such as TensorFlow, PyTorch, or openVINO.
- Experience with GenAI or Large Language Models or NLP for training, fine-tuning, and evaluating LLMs
- Experience in computer vision algorithms and frameworks (e.g., OpenCV, Detectron2, YOLO).
- Strong knowledge of machine learning algorithms (supervised, unsupervised, reinforcement learning).
- Familiarity with deploying AI models in offline environments and working with locally hosted models.
- Strong mathematical/statistical skills and the ability to clearly understand, define and communicate complex concepts in this area.
- Programming Skills:
- Advanced proficiency in Python and relevant AI/ML libraries (NumPy, SciPy, scikit-learn).
- Familiarity with version control tools (e.g., Git).
- Experience with C++ along with Docker, Kubernetes, or other containerization technologies is a plus.
- Problem Solving: Strong analytical and problem-solving skills with the ability to tackle complex and unstructured problems in AI and computer vision.
- Communication Skills: Ability to communicate complex technical concepts clearly to both technical and non-technical stakeholders.