AI Engineer (Python)

Who we are

We are looking for Artificial Intelligence Engineering Experts to lead the design, industrialization, and continuous improvement of production AI capabilities for our platform. The role will provide deep hands-on expertise across machine learning, generative AI, data and model pipelines, evaluation, deployment, MLOps or LLMOps, observability, security, and operational reliability.

The experts will work with Product, Architecture, Data, Engineering, Cybersecurity, Compliance, Quality Assurance, DevOps, and business stakeholders to identify viable use cases and convert experimentation into governed, measurable, maintainable services. They will establish technical standards, challenge weak assumptions, manage AI-specific risks, and mentor teams in sound engineering and scientific practices.

What you'll be doing

• Assess proposed AI use cases for technical feasibility, data readiness, expected value, operational constraints, risk, and appropriate build, buy, or reuse options
• Define AI engineering reference architectures, reusable components, lifecycle standards, guardrails, and decision criteria for machine-learning and generative-AI solutions
• Lead solution design across data preparation, feature or context engineering, model selection, experimentation, training or fine-tuning, evaluation, serving, integration, and monitoring
• Develop and optimize production AI components, including predictive models, retrieval pipelines, embeddings, vector search, generative workflows, tool use, and agentic orchestration where justified
• Establish reproducible experimentation, source control, dataset and model versioning, metadata capture, benchmark management, and clear promotion criteria
• Design and implement MLOps or LLMOps pipelines for automated testing, packaging, security checks, deployment, rollback, model or prompt promotion, and environment consistency
• Create rigorous evaluation frameworks covering task performance, robustness, groundedness, hallucination, safety, bias, explainability, latency, scalability, and cost as applicable
• Define production monitoring for model and data quality, drift, usage, errors, safety events, service performance, resource consumption, and business outcomes
• Apply security, privacy, responsible-AI, and regulatory-by-design controls, including data minimization, access control, provenance, human oversight, misuse prevention, and auditable records
• Lead technical reviews, proofs of value, architecture decisions, technology assessments, and root-cause analyses for material AI incidents or performance degradation
• Collaborate with application engineers to expose AI capabilities through reliable APIs, services, event flows, and user experiences with appropriate fallback behavior
• Document data assumptions, model choices, experiments, limitations, evaluation evidence, dependencies, operational procedures, risks, and technical decisions
• Mentor AI, data, and software engineers, promote knowledge sharing, and raise engineering maturity across delivery teams
• Track relevant AI engineering developments and recommend adoption only when evidence demonstrates value, feasibility, and acceptable risk

What you'll bring along

• Deep knowledge of machine-learning engineering, model development lifecycles, statistics, supervised and unsupervised learning, deep learning, and production inference
• Strong hands-on Python experience and familiarity with frameworks such as scikit-learn, PyTorch, TensorFlow, Hugging Face, or equivalent technologies
• Advanced understanding of generative-AI engineering, large language models, embeddings, retrieval-augmented generation, vector stores, prompt or context engineering, tool calling, and agent orchestration
• Experience with data preparation, feature engineering, data quality, lineage, distributed processing, SQL or NoSQL stores, and secure access to enterprise information
• Experience with MLOps or LLMOps practices, experiment tracking, model registries, pipeline orchestration, containerization, cloud AI services, and automated deployment
• Strong knowledge of model evaluation, benchmarking, error analysis, drift detection, explainability, robustness, safety testing, and human-in-the-loop validation
• Understanding of AI security, privacy, responsible-AI, governance, intellectual-property, and regulatory control considerations
• Ability to design reliable distributed services, APIs, batch or streaming pipelines, observability, capacity models, and production-support processes for AI workloads
• Strong scientific reasoning, systems thinking, technical leadership, documentation, and communication skills
• Ability to explain complex AI trade-offs to engineering, product, risk, compliance, and executive stakeholders
• Professional working proficiency in English to understand technical and scientific documentation and communicate effectively with international teams
• Proven expert-level track record designing, delivering, and operating production machine-learning or generative-AI systems
• Experience working with AI assistant platforms and CLI tools
• Demonstrable ability to move from experimentation to reliable, monitored, secure, and maintainable production services
• Experience providing technical leadership across data, AI, Agent development, software engineering, architecture, platform, security, and governance stakeholders
• Strong hands-on engineering capability; profiles limited to research, strategy, or demonstrations without production ownership will not be sufficient
• Ability to make evidence-based decisions under uncertainty and to define measurable acceptance and operational criteria for AI behavior
• Proactive, rigorous, collaborative, and continuous-improvement mindset with strong ownership of AI quality and operational risk
• Relevant AI, machine-learning, cloud, data, security, or architecture certifications are desirable but not mandatory