Senior AI Engineer - GenAI, insurance or reinsurance, MLOps, Python/PySpark
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Contract Type:
Freelance (B2B)
Duration:
6-12 months initial contract, extension possible
Daily rate:
Negotiable
The AI Engineer is responsible for engineering, operationalizing, and governing AI solutions in production. While the role requires strong exposure to data science and model behavior, it does not require deep Data Scientist specialization. The focus is on turning models and experiments into reliable, well-governed production systems.
Responsibilities
Own and maintain prompt engineering strategies, including prompt versioning, testing, and optimization
Design AI workflows combining models, prompts, tools, enterprise data, and business logic
Implement AI orchestration layers to manage multi-step reasoning, decisioning, and actions
Ensure AI systems integrate cleanly into business workflows, APIs, and user interfaces
Apply guardrails to ensure safe, explainable, and compliant AI behaviour
MLOps
Support building and maintaining production-grade deployment pipelines for AI solutions
Ensure reliability, scalability, cost control, and latency optimisation
Implement monitoring and observability for AI systems (usage, performance, drift, failures)
Define and enforce change control, versioning, rollback, and release management processes
Collaboration with Data Science & Business
Collaborate closely with data scientists, actuaries, and other business functions
Validate model behaviour, outputs, and assumptions from a production and business-use perspective
Communicate engineering limitations and operational considerations to stakeholders
Required Skills & Experience
Core Skills
5+ years of experience in AI/ML engineering, advanced analytics, or advanced software engineering roles
2+ years hands-on experience building, calibrating, and operationalising AI/ML solutions
Strong algorithmic and problem-solving skills
Strong programming skills in Python / PySpark and strong SQL expertise
Exposure to Data Science methods for validating AI models
Palantir Foundry and AIP experience
LLM & Advanced AI Capabilities
Hands-on experience with prompt engineering, prompt testing, and prompt lifecycle management
Experience implementing RAG architectures or similar approaches
Experience with AI orchestration frameworks, agentic patterns, and tool xpzdshu / function calling
MLOps
Strong understanding of model evaluation, calibration techniques, and monitoring
Familiarity with model explainability, fairness, and robustness
Experience with MLOps tooling and practices
Platforms & Tools
Experience working in cloud environments (AWS, Azure, or GCP)
Experience integrating AI models into production systems with monitoring, logging, and alerting
Experience working with large data sets on enterprise data platforms and distributed computing (Spark / Hive / Hadoop preferred)
Nice to Have
1–2 years exposure to insurance or reinsurance
Experience with agentic AI frameworks and enterprise-scale AI platforms
Experience working in insurance / reinsurance (Underwriting, Claims, Inforce management for L&H)
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