Role Purpose:
As a Lead AI Engineer at Prevalent AI, you will lead the design, architecture, and delivery of production-grade Generative AI solutions across our Exposure Management and Data Fabric platforms. You will provide technical leadership for AI initiatives, drive architectural decisions, establish engineering best practices, and remain hands-on in building complex AI systems.
You will own the end-to-end lifecycle of AI capabilities including Retrieval-Augmented Generation (RAG), multi-agent systems, LLM-backed APIs, AI safety frameworks, evaluation pipelines, LLMOps, and cloud-native AI services, ensuring they are scalable, secure, observable, and production-ready.
This role is suited for engineers who have successfully delivered complex AI solutions in production, can independently drive technical initiatives from concept to deployment, and provide technical leadership across multiple AI projects.
Key Accountabilities:
- Lead the architecture, design, and delivery of complex production-grade AI solutions including RAG pipelines, AI agents, vector search, LLM-powered services, and agentic AI workflows.
- Independently drive AI initiatives from technical discovery through production deployment, making architectural decisions and ensuring successful delivery.
- Build scalable AI applications using FastAPI, LangChain, LangGraph, MCP, vector databases, cloud AI services, and modern AI engineering practices.
- Design robust prompting, retrieval, fine-tuning, evaluation, and AI safety strategies to improve accuracy, reliability, latency, and cost.
- Establish LLMOps practices including prompt lifecycle management, experiment tracking, model versioning, AI evaluation, production monitoring, and governance.
- Build and deploy AI services using CI/CD pipelines, Docker, Kubernetes, cloud platforms, and where appropriate, self-hosted/open-source LLMs.
- Collaborate closely with Product, Platform Engineering, Data Engineering, Backend Engineering, and client-facing teams to translate business requirements into scalable AI solutions.
- Provide technical leadership through architecture reviews, mentoring, engineering best practices, and technical guidance across AI initiatives.
- Produce high-quality technical documentation covering solution architecture, AI workflows, evaluation approaches, API contracts, deployment processes, and operational runbooks.
Skills & Experience:
Must Have
- Demonstrated experience leading the design and successful delivery of complex production-grade Generative AI solutions with end-to-end ownership.
- Proven ability to independently drive technical decisions, lead AI initiatives, and deliver scalable AI solutions across cross-functional teams.
- Strong hands-on experience with LangChain, LangGraph, RAG, AI agents, vector databases, FastAPI, Python, and modern LLM application development.
- Experience building AI applications using Azure OpenAI, AWS Bedrock, Google Vertex AI/Gemini, or equivalent cloud AI platforms.
- Demonstrated experience of mentoring engineers, conducting architecture reviews, and providing technical leadership on enterprise AI projects.
Good to Have
- Experience with FastMCP or advanced MCP ecosystems.
- Experience deploying self-hosted/open-source LLMs (Llama, Mistral, Qwen, Phi, etc.) on GPU infrastructure or on-premise environments.
- Experience implementing prompt engineering, fine-tuning (LoRA/QLoRA/PEFT), AI guardrails, hallucination mitigation, and structured AI workflows.
- Experience with LLMOps practices including prompt lifecycle management, model versioning, AI evaluation (RAGAS, DeepEval, LangSmith or equivalent), production monitoring, and CI/CD.
- Experience deploying AI workloads using Docker, Kubernetes, and modern cloud-native engineering practices.
- Experience implementing AI observability using LangFuse, LangSmith, OpenTelemetry, Grafana, or equivalent tools.
- Exposure to cybersecurity, security analytics, or enterprise AI platforms.