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Senior GenAI Engineer
Fractal Analytics is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets. An ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite empowers imagination with intelligence. And that it will be such Fractalites that will continue to build the company for the next 100 years.
Please visit Fractal | Intelligence for Imagination for more information about Fractal.
Location: Onsite Cambridge, MA
Role Summary
We are looking for an experienced GenAI Senior Engineer to design, build, and maintain scalable, secure, and production-grade AI platforms and applications. The role will focus on Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), AWS Cloud , and modern platform engineering practices. The ideal candidate should possess deep expertise in designing enterprise-grade AI solutions using Amazon Bedrock , LLM orchestration frameworks, cloud-native architectures, and infrastructure automation.
The candidate will play a key role in enabling enterprise teams to develop, deploy, monitor, and govern AI applications while ensuring scalability, security, observability, and operational excellence.
Key Responsibilities
· Design, develop, and maintain scalable, secure, and highly available GenAI platforms and AI-powered applications on AWS.
· Architect and implement Agentic AI solutions , multi-agent systems, tool integrations, function calling, and workflow orchestration frameworks.
· Design and implement enterprise-grade RAG (Retrieval-Augmented Generation) solutions using embeddings, vector databases, semantic search, reranking, and knowledge retrieval techniques.
· Build AI applications leveraging Amazon Bedrock for LLM inference, model orchestration, prompt management, and foundation model integrations.
· Develop reusable AI platform components, SDKs, APIs, accelerators, and reference architectures.
· Build knowledge-driven AI systems utilizing vector stores, graph databases, and knowledge graph technologies.
· Create evaluation and benchmarking frameworks to measure AI quality, accuracy, latency, safety, and performance.
· Implement AI governance, security controls, responsible AI practices, guardrails, and compliance mechanisms.
· Develop observability capabilities including telemetry, monitoring, tracing, logging, and operational dashboards.
· Design and develop cloud-native microservices and APIs using Python and modern software engineering practices.
· Automate cloud infrastructure provisioning and deployment using Terraform and Infrastructure as Code (IaC) principles.
· Build and maintain CI/CD pipelines for AI and cloud workloads.
· Collaborate with cross-functional engineering, security, product, and business teams to drive AI platform adoption and best practices.
· Mentor junior engineers and contribute to architecture reviews and technical leadership initiatives.
Required Skills
· 8–10 years of software engineering experience with at least 3+ years in GenAI/AI solution development.
· Strong hands-on experience building production-grade Generative AI solutions .
· Expertise in Agentic AI , AI Agents, multi-agent architectures, tool calling, function calling, orchestration, and MCP (Model Context Protocol).
· Strong experience designing and implementing RAG architectures , including embeddings, vector databases, retrieval pipelines, reranking, and semantic search.
· Hands-on experience with Amazon Bedrock , foundation model integration, inference optimization,…