• Define and implement scalable, reproducible, monitorable, production-ready Machine Learning architectures. • Develop, evolve, and maintain production Machine Learning pipelines and services, ensuring reliability and performance. • Deploy highly available models and pipelines with a focus on MLOps, CI/CD, and automation. • Collaborate with data scientists, data engineers, developers, and business stakeholders. • Diagnose and resolve complex issues related to models and pipelines in production. • Lead technical discussions and workshops, and support architectural decisions with teams and clients. • Contribute to raising the client's and A3 Data's technical maturity by promoting best practices.

• Develop, train, and improve Machine Learning models, ensuring reproducibility, scalability, and production monitoring; • Implement and manage the model lifecycle, with versioning for code, data, metrics, and artifacts, following MLOps best practices; • Package models as scalable, highly available services integrated into automated pipelines; • Support and continuously improve ML solutions in production, identifying and fixing issues; • Collaborate with Data Engineering, Data Science, and business teams in a multidisciplinary environment; • Perform code reviews and support the technical development of more junior engineers; • Participate in technical discussions with clients, explaining solutions, architectural decisions, and trade-offs.

• Design, adapt, and optimize deep learning architectures for scientific domains and data modalities. • Own and deliver on complex ML projects, including experiment design, implementation, evaluation, and iteration based on results. • Write clean, well-tested code in PyTorch and NumPy enabling a high experimentation rate. • Stay current with deep learning research and its applications in chemistry and biology. • Propose and prototype new ideas to enhance our modeling capabilities. • Work closely with scientists and engineers across the team to integrate models into our product and infrastructure.

• Design, develop, and deploy AI/ML models and pipelines that meet mission and performance objectives. • Build, train, and fine-tune models using frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, and LangChain. • Develop and operationalize MLOps pipelines (MLflow, Kubeflow, DVC, or custom training/inference orchestration). • Implement and optimize vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval architectures (RAG, graph, hybrid). • Write clean, efficient Python code for data ingestion, feature engineering, embeddings, and inference services. • Experiment with fine-tuning and optimization of LLMs and task-specific models (LoRA, QLoRA, PEFT). • Contribute to agent-based applications using frameworks like LangGraph, AutoGen, CrewAI, or DSPy. • Integrate AI services into real-world systems via APIs, event-driven workflows, or UI copilots. • Collaborate with data engineers, software developers, and mission analysts to ensure AI models are production-ready and aligned with customer needs. • Participate in peer reviews, contribute to shared repositories, and document models and experiments for reproducibility.