Let’s create WOW together

What will your role look like

• Collaborate effectively in a multinational environment, as the role involves cross-region collaboration with teams worldwide.
• Engage with and understand complex customer-specific business processes
• Develop and maintain custom solutions using ABAP and ABAP OO.
• Analyze and engage with complex, customer-specific business processes.
• Translate business requirements into technical solutions.

Why you will love this role

• Besides a competitive package, an open workspace full of smart and pragmatic team members, with ever-growing opportunities for professional and personal growth
• Be a part of a learning culture where teamwork and collaboration are encouraged, diversity is valued and excellence, compassion, openness and ownership is rewarded.

We would like you to bring along

• Proven experience in SAP development on SAP ECC and strong understanding of related technologies
• Strong ABAP and ABAP OO proficiency
• Strong Knowledge of Smart Forms and SAPScript as well as interface technologies such as ALE and SOAP Services
• Comprehensive knowledge of SAP Sales and Distribution (SD)
• Ideally knowledge of SAP Material Management and SAP BRIM, especially SAP Convergent Invoicing (CI) and SAP Convergent Charging (CC)
• Experience in customer advisory and consulting, effectively translating business requirements into technical solutions
• Ability to produce clear and concise technical documents
• Excellent communication skills to facilitate collaboration and knowledge sharing
• Experience with mentoring and developing of young professionals.

What will your role look like

• Design, train, evaluate, and deploy traditional ML models as well as Generative AI-based applications.
• Work on supervised, unsupervised, and deep learning models including regression, classification, clustering, and sequence models.
• Build end-to-end ML pipelines including data preprocessing, feature engineering, model training, hyperparameter tuning, and evaluation.
• Develop and optimize LLM-based workflows using LangChain, LangGraph, and orchestration frameworks.
• Fine-tune, evaluate, and integrate LLMs such as GPT, LLaMA, Claude, Mistral, Falcon, Cohere, and Gemini.
• Implement Retrieval-Augmented Generation (RAG) using embeddings and vector stores like FAISS, Pinecone, or Chroma.
• Apply prompt engineering, LoRA, PEFT, and adapter-based fine-tuning to optimize LLMs for specific tasks.
• Build Agentic AI systems with tool-use capabilities and reasoning chains (e.g., ReAct, AutoGPT, BabyAGI, CrewAI).
• Use Hugging Face for leveraging pre-trained models and datasets for rapid experimentation.
• Collaborate with product, data, and engineering teams to productionize AI solutions using scalable cloud infrastructure.

Why you will love this role

• Besides a competitive package, an open workspace full of smart and pragmatic team members, with ever-growing opportunities for professional and personal growth
• Be a part of a learning culture where teamwork and collaboration are encouraged, diversity is valued and excellence, compassion, openness and ownership is rewarded.

We would like you to bring along

• Strong grasp of core ML concepts such as model selection, evaluation metrics, bias/variance tradeoff, overfitting/underfitting, etc.
• Good exposure on using Azure Open AI and hosting applications in Azure environment
• Experience in building and tuning models using scikit-learn, XGBoost, LightGBM, TensorFlow, or PyTorch.
• Experience in building ML pipelines with feature engineering, model tuning, cross-validation, and A/B testing.
• Proficiency in LangChain, LangGraph, and integrating with Hugging Face Transformers
• Ecosystem.
• Deep knowledge of various LLMs and techniques like prompt engineering, few-shot learning, and instruction tuning.
• Experience in building Agentic AI systems and coordinating multi-agent flows or tool-chaining.
• Familiarity with LLMOps/MLOps tools (e.g., MLflow, Weights & Biases, Kubeflow, SageMaker, or Vertex AI).
• Strong programming skills in Python, and experience deploying models using FastAPI, Flask, or Streamlit.
• Experience with cloud platforms (AWS/GCP/Azure) and handling GPU/TPU resources.
• Solid understanding of data structures, algorithms, and software engineering best Practices. 
• Highly skilled AI/ML Engineer with a solid foundation in Machine Learning and deep hands-on experience in Generative AI (GenAI). 
• Strong capabilities in building, training, and deploying ML models, along with significant experience.
• working with frameworks such as LangChain, LangGraph, and platforms like Hugging Face, vector databases, and various LLMs. You’ll be a key contributor in developing smart assistants, AI agents, and ML solutions that solve complex business problems.
• Experience with LangSmith, PromptLayer, or other LLM observability tools
• Familiarity with Guardrails.AI, semantic caching, and output validation techniques
• Exposure to multi-modal models like CLIP, DALL·E, Stable Diffusion, or Whisper
• Contributions to open-source GenAI or ML libraries/projects
• Domain expertise in areas like healthcare, finance, manufacturing, or legal tech.

What will your role look like

• Design AI solutions using both traditional ML and GenAI approaches based on problem needs
• Lead end-to-end implementation of ML projects including data preparation, model development, deployment, and monitoring
• Build GenAI-based applications using LLMs, embeddings, and multi-step reasoning frameworks (e.g., LangChain, LangGraph)
• Conduct research and proof-of-concepts to evaluate emerging GenAI models and techniques
• Guide and mentor junior engineers; review designs, architecture, and code quality
• Develop fine-tuned and instruction-tuned LLMs using platforms like Hugging Face, OpenAI, Cohere, etc.
• Build agentic AI systems involving tool use, memory, and reasoning frameworks
• Ensure AI models are robust, explainable, and cost-efficient in production
• Collaborate with stakeholders to understand business goals and translate them into technical solutions
• Communicate complex AI concepts to both technical and non-technical audiences.
• This is a strategic role where you will play a key part in shaping our AI offerings, guiding teams, and solving complex problems using both modern and established AI techniques.

Why you will love this role

• Besides a competitive package, an open workspace full of smart and pragmatic team members, with ever-growing opportunities for professional and personal growth
• Be a part of a learning culture where teamwork and collaboration are encouraged, diversity is valued and excellence, compassion, openness and ownership is rewarded.

We would like you to bring along

• Experienced AI/ML Engineer with a strong background in both traditional Machine Learning and Generative AI. 
• Experience in classical AI/ML and 2–3 years in GenAI, total 10 years of experience with the ability to design, research, and build scalable AI solutions across domains. 
• 6–7 years in traditional ML/DL, including regression, classification, clustering, NLP, time series forecasting, and deep learning techniques such as feedforward networks, CNNs, RNNs, LSTMs, GRUs, and attention-based models for structured, unstructured, and sequential data
• 2–3 years in Generative AI (LLMs, embeddings, RAG, fine-tuning, prompt engineering)
• Experience with Python, scikit-learn, TensorFlow, PyTorch, and Hugging Face Transformers
• Understanding of ML lifecycle: data wrangling, feature engineering, model training, evaluation, and deployment
• Deep experience with LLMs such as GPT, LLaMA, Mistral, Claude, or Gemini and tools like LangChain, LangGraph, and LLamaIndex
• Experience building agentic AI solutions (e.g., AutoGPT, ReAct, CrewAI, BabyAGI)
• Proficiency with vector databases (FAISS, Pinecone, Weaviate, Chroma) and embedding models (OpenAI, Cohere, BGE, etc.)
• Excellent communication and presentation skills
• Strong research mindset with the ability to quickly learn and adapt to new GenAI technologies
• Ability to assess trade-offs between classic and generative AI techniques and recommend the best-fit approach