LLM Engineer

Why this role mattersWe’re building production-grade GenAI systems at the core of trading, risk, and enterprise decision-making. This is not a demo lab or a prompt-only role—you’ll design and ship real LLM platforms used by traders, compliance teams, and leadership every day.If you enjoy solving hard problems at the intersection of LLMs, distributed systems, security, and real-world constraints, this role gives you scale, ownership, and impact.What you’ll buildAs a Senior LLM Engineer, you’ll own the end-to-end lifecycle of Large Language Model–powered systems—from architecture to production operations.You’ll work on:LLM-native platforms for trading intelligence, risk signals, compliance automation, fraud detection, and enterprise knowledge systemsRAG and agentic workflows that integrate market data, internal systems, and secure toolsHigh-performance, cost-aware inference stacks running in hybrid and multi-cloud environmentsEvaluation, safety, and governance frameworks that make LLMs reliable in regulated financial environmentsWhat you’ll do🔹 Architecture & EngineeringDesign and ship end-to-end LLM systems: prompt orchestration, RAG pipelines, agents/tool-use, and workflow automationBuild low-latency, scalable APIs and microservices for inference and orchestrationImplement LLMOps/MLOps pipelines: model versioning, prompt CI/CD, experiment tracking, and automated A/B testing🔹 Data & Retrieval SystemsEngineer secure ingestion pipelines for market data, trades, compliance records, and voice/text transcriptsDesign vector search systems with smart chunking, hybrid retrieval (BM25 + embeddings), re-ranking, and auditability🔹 Evaluation, Safety & Model RiskDefine how “good” looks: hallucination detection, task-level metrics, adversarial testing, and red-teamingBuild human-in-the-loop validation, bias/fairness checks, and explainability where requiredHelp formalize model risk management for LLMs in production🔹 Security & Compliance (Done Right)Work with Security and Risk teams to embed Zero Trust, least-privilege access, PII controls, and audit trailsImplement guardrails: content filters, policy enforcement, safe tool invocation, and full traceability🔹 Observability & Cost EngineeringInstrument everything: latency, throughput, token usage, prompt drift, errors, and SLOsActively optimize cost using model selection, quantization, caching, batching, and autoscaling🔹 Technical LeadershipPartner with Trading, Risk, Compliance, Infra, and Platform teamsMentor engineers on LLM patterns, prompt engineering, and production best practicesContribute to internal platforms, reusable components, and engineering standardsWhat we’re looking forExperience5–10+ years in ML / platform / backend engineering3+ years building and operating production LLM systemsStrong hands-on skills in:Languages: Python (primary), plus TypeScript / Go / JavaLLM frameworks: LangChain, LlamaIndex, Semantic Kernel, DSPy (or similar)RAG systems: FAISS, Milvus, Pinecone; hybrid search; cross-encoder re-rankingModel ecosystems: OpenAI / Azure OpenAI, Anthropic, Vertex AI; open-source (Llama, Mistral, Phi)Inference optimization: vLLM, Triton, quantization (GPTQ/AWQ), batching, cachingLLMOps/MLOps: MLflow or W&B, model registries, CI/CD, feature storesCloud & infra: Docker, Kubernetes, Terraform, event-driven systemsObservability: Prometheus, Grafana, OpenTelemetry (metrics, logs, traces)Security fundamentals: OAuth/OIDC, RBAC, secrets management, encryption, data governanceWhy you’ll enjoy working hereReal production impact—no toy demosHard engineering problems with clear ownership and scaleFreedom to choose the right models and architectures, not just the trendy onesA chance to define how GenAI is done responsibly in financial services