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.

 

·      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.

 

·      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.

 

·      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).

 

·      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.

 

·      Develop evaluation frameworks for accuracy, hallucination detection, and model performance

·      Optimize latency, throughput, and cost for production deployments

·      Establish benchmarking and observability standards

 

·      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).

 

·      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.