We are seeking a
Senior AI Engineer to design, build, and scale enterprise-grade AI platforms leveraging frontier Large Language Models (LLMs). This role sits at the intersection of AI engineering, platform architecture, and applied GenAI, with a strong emphasis on productionization in regulated environments (financial services, wealth, capital markets).You will play a key role in operationalizing AI at scale, building reusable capabilities, and enabling secure, governed adoption of LLM-powered solutions across the enterprise.
Key ResponsibilitiesAI Platform Engineering
- Design and build scalable AI platforms supporting LLMs, RAG pipelines, and multi-model orchestration· Develop reusable frameworks for prompt management, model routing, evaluation, and monitoring· Implement LLMOps / MLOps pipelines for continuous integration, deployment, and lifecycle management· Architect API-first AI services for enterprise-wide consumption. Frontier LLM Integration
- Integrate and optimize models from providers like OpenAI, Anthropic, Google DeepMind, and open-source ecosystems· Build multi-model strategies (closed + open source) for performance, cost, and governance· Implement advanced techniques:
- Retrieval-Augmented Generation (RAG)
- Tool use / agents· Fine-tuning and embeddings· Context optimization and memory systems. Enterprise AI & Governance
- Design systems aligned with security, compliance, and data privacy requirements· Implement guardrails, auditability, and explainability in AI workflows· Enable safe AI deployment in distributed environments (e.g., advisor desktops, hybrid cloud). Applied AI Solutions
- Build AI-driven use cases such as:
- Intelligent document processing (e.g., wealth plans, research docs)
- Advisor copilots and decision support systems· Knowledge assistants and enterprise search· Partner with business teams to translate use cases into scalable AI solutions. Performance & Evaluation
- Develop evaluation frameworks for accuracy, hallucination detection, and model performance· Optimize latency, throughput, and cost for production deployments· Establish benchmarking and observability standards Required Qualifications
- 7-12+ years in software engineering, with 3+ years in AI/ML engineering or GenAI· Strong proficiency in:
- Python, APIs, microservices architecture· LLM frameworks (LangChain, LlamaIndex, etc.)
- Hands-on experience with:
- RAG pipelines, vector databases (Pinecone, FAISS, etc.)
- Cloud platforms (AWS, Azure, GCP)
- Deep understanding of transformer models, LLM architecture, prompt engineering, and context handling· Experience building production-grade AI systems (not just POCs). Preferred Qualifications
- Experience in financial services / wealth / capital markets· Familiarity with regulated AI deployments (compliance, DLP, governance)
- Exposure to agentic AI systems and autonomous workflows· Experience with fine-tuning / LoRA / model optimization· Knowledge of data engineering pipelines and real-time architectures
