We are looking for a Lead AI Engineer to help shape and drive AI/ML enablement and readiness across the organization. This role requires strong data engineering fundamentals: you will start hands-on, contributing directly to our data platform and pipelines, while progressively taking on a leading role in defining how the organization builds, deploys, and governs AI/ML capabilities.
Reporting directly to the VP, Head of Data, you will work autonomously to identify gaps, propose solutions, and bring innovative thinking to how our data and AI/ML ecosystem should evolve. You will partner closely with Data Governance, Data Engineering, and Product stakeholders to define our AI/ML frameworks and MLOps strategy, and to ensure the organization is well-positioned to adopt AI/ML responsibly and at scale.
Key Accountabilities/Deliverables:- Design, build, and optimize data pipelines, ingestion frameworks, and platform components that support analytics, reporting, and AI/ML use cases.
- Take direct, autonomous ownership of complex engineering initiatives, from technical design through implementation and rollout, with minimal need for oversight.
- Identify and resolve performance, scalability, and reliability issues across the existing data platform.
- Bring innovative, well-reasoned solutions to data engineering problems, proactively identifying gaps and proposing improvements rather than waiting for direction.
- Write clean, well-tested, well-documented code and infrastructure-as-code, maintaining strong engineering hygiene across your work.
- Help define the organization's AI/ML frameworks, evaluating and recommending tools, platforms, and standards for building and deploying AI/ML solutions.
- Build working prototypes that provide immediate value to the engineering teams
- Shape and help implement our MLOps strategy, including approaches to model deployment, monitoring, versioning, and lifecycle management
- Partner in deep, ongoing collaboration with Data Governance to ensure AI/ML frameworks and practices align with data governance, security, and compliance standards.
- Design and advocate for data infrastructure patterns that support AI/ML use cases at scale (e.g., feature stores, curated/governed datasets, streaming access for training and inference).
- Partner with Data Science, Data Engineering, and business stakeholders to assess AI/ML readiness gaps and build a roadmap to close them.
- Act as a subject-matter expert and thought partner to the VP, Head of Data on emerging AI/ML technologies, practices, and industry trends.
- Document AI/ML standards, frameworks, and decisions to support consistent adoption across the organization as the practice matures.
- Act as a senior technical resource for the team, providing guidance on architecture, design patterns, and best practices AI/ML readiness and ML Ops frameworks
- Partner closely with Enterprise Architecture on establishing architectural blueprints for AI readiness
- Other Duties as Assigned.
Technical Knowledge and Understanding:Data Engineering- Strong data engineering fundamentals: deep expertise in data pipeline design, optimization, and distributed data processing (e.g., Spark, dbt, Airflow, Kafka, or equivalent).
- Platforms: hands-on experience with Snowflake, Databricks, and/or Azure Synapse Analytics, with the ability to architect and optimize workloads on one or more of these platforms.
- Strong knowledge of cloud platforms (AWS, Azure, or GCP) and modern data warehouse/lakehouse architectures.
AI/ML EngineeringProgramming & software engineering fundamentals
- Strong Python (the de facto language for AI/ML tooling); solid software engineering practices (testing, version control, code review) since AI engineers ship production systems, not just notebooks
- API design and integration - most AI engineering work today is building systems around models (orchestration, tool-calling, retrieval), not training them from scratch
LLM & foundation model fluency
- Practical experience with LLM APIs (ie. OpenAI) and open-weight models
- Prompt engineering and prompt evaluation as a discipline, not just trial-and-error
- Understanding of context windows, tokenization, embeddings, and model limitations (hallucination, latency, cost tradeoffs)
RAG (Retrieval-Augmented Generation) & data retrieval
- Vector databases (Pinecone, Weaviate, pgvector, etc.) and embedding models
- Chunking strategies, hybrid search, reranking
Agentic systems & orchestration
- Frameworks like LangChain, LangGraph, LlamaIndex, or custom orchestration
- Tool-use / function-calling design, multi-step reasoning chains, agent memory and state management
Fine-tuning & model adaptation
- When to fine-tune vs. prompt vs. RAG
- Familiarity with parameter-efficient methods (LoRA, etc.) MLOps / LLMOps
- Model evaluation frameworks, A/B testing for model outputs, observability (tracing, logging model calls)
- Deployment patterns: latency/cost optimization, caching, streaming responses, fallback handling
- Versioning prompts and models, not just code
- Safety, evaluation & governance awareness
- Bias/safety evaluation, guardrails, handling PII appropriately
Experience:- Minimum 7+years of experience in data engineering, with experience working on large-scale, mature data platforms.
- 3+ years of experience developing ML or AI deliverables - includes deployment to production
- Bachelor's degree in related field or demonstrated equivalent experience in a related field required.
- Working knowledge of Agentic Workflows for engineering and architecture
- Demonstrated experience taking autonomous technical ownership of complex projects from design through delivery, with minimal oversight.
- Experience contributing to or shaping AI/ML enablement efforts, such as defining frameworks, evaluating MLOps tooling, or building infrastructure that supports model training and deployment.
- Experience partnering with Data Governance, Data Science, or Compliance teams to align technical practices with governance and regulatory requirements.
- A track record of proposing and driving innovative technical solutions rather than simply executing predefined plans.
- Experience designing or implementing Agentic workflows for data engineering preferred.
- Experience working with Property & Casualty insurance carriers preferred.
- Experience with Data Vault 2.0 or Ensemble data modeling techniques preferred.
#LI-Hybrid
At Core Specialty, you will receive a competitive salary and opportunities for professional development and advancement. We offer medical, dental, vision, and life insurances; short and long-term disability; a Company-match of 100% of a 6% contribution 401(k) plan; an Employee Assistance Plan; Health Savings Account, Flexible Spending Account, Health Reimbursement Account, and a wellness program
