Senior Gen AI Engineer

Fractal Analytics•Massachusetts, MA

2d•$120,000 - $150,000•Onsite

About The Position

Fractal Analytics is seeking an experienced Senior GenAI Engineer to design, build, and maintain scalable, secure, production-grade AI platforms and applications. The role focuses on Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), AWS Cloud, and modern platform engineering. The engineer will design enterprise-grade AI solutions using Amazon Bedrock, LLM orchestration frameworks, cloud-native architectures, and infrastructure automation. The role will help enterprise teams develop, deploy, monitor, and govern AI applications while ensuring scalability, security, observability, and operational excellence. It is based onsite in Cambridge, Massachusetts, and involves collaboration across engineering, security, product, and business teams, as well as mentoring junior engineers and providing technical leadership.

Requirements

8–10 years of software engineering experience, including 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, prompt engineering, and model orchestration.
Deep understanding of LLMs, prompt engineering, evaluation, model selection, and deployment patterns.
Strong proficiency in Python for backend development, AI application development, and API implementation.
Strong experience with AWS services such as Bedrock, Lambda, ECS/EKS, API Gateway, S3, IAM, CloudWatch, DynamoDB, OpenSearch, and Step Functions.
Experience with Terraform and Infrastructure as Code for cloud automation and platform deployment.
Experience building cloud-native applications using APIs, microservices, Docker, Kubernetes, and CI/CD pipelines.
Understanding of AI observability, monitoring, tracing, and telemetry.
Knowledge of relational, NoSQL, vector, and graph databases.
Experience with security, governance, compliance, and responsible AI frameworks.
Strong communication, stakeholder management, problem-solving, analytical thinking, collaboration, ownership, and accountability skills.
Ability to provide technical leadership, mentoring, and architecture guidance; document and share knowledge; and work independently in a fast-paced client-facing environment.

Nice To Haves

Experience with LangChain, LangGraph, or Semantic Kernel.
Experience with LlamaIndex.
Experience with OpenAI Agents SDK or similar agent frameworks.
Experience with Amazon OpenSearch, Pinecone, Weaviate, Chroma, FAISS, or similar vector database technologies.
Experience with OpenTelemetry, Langfuse, Arize Phoenix, or MLflow.
Experience with Grafana, Prometheus, or CloudWatch Observability.
Experience with Neo4j and knowledge graph technologies.
Experience with distributed systems and scalable platform engineering.
Experience with MLOps, LLMOps, and AI deployment pipelines.
Exposure to fine-tuning, model evaluation, and AI benchmarking frameworks.

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 solutions using embeddings, vector databases, semantic search, reranking, and knowledge retrieval techniques.
Build AI applications using 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 using 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 principles.
Build and maintain CI/CD pipelines for AI and cloud workloads.
Collaborate with 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.

Benefits

Health, dental, vision, life insurance, and disability plans, subject to plan terms.
Benefits eligibility begins on the first day of employment.
Eligibility to participate in the Company 401(k) Plan after 30 days of employment.
11 paid holidays.
12 weeks of parental leave.
Free-time PTO policy allowing flexibility for sick time or vacation.
May be eligible for a discretionary bonus for the current performance period.

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