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)
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

AI Engineer

Key skills