Senior Gen AI Engineer

Sourced from fractal.wd1.myworkdayjobs.com

Fractal Analytics seeks a Senior GenAI Engineer to design and build production-grade generative AI platforms and applications on AWS. The role centers on Agentic AI, RAG architectures, Amazon Bedrock, and cloud-native platform engineering. You will enable enterprise teams to develop, deploy, monitor, and govern AI applications with strong scalability, security, and observability.

• Design and build scalable, secure GenAI platforms and AI applications on AWS
• Architect Agentic AI solutions, multi-agent systems, tool/function calling, and orchestration
• Implement enterprise-grade RAG with embeddings, vector databases, and semantic search
• Build AI applications on Amazon Bedrock for inference, prompt management, and model orchestration
• Develop reusable platform components, SDKs, APIs, and reference architectures
• Create evaluation and benchmarking frameworks for AI quality, latency, and safety
• Implement AI governance, guardrails, security, and responsible AI practices
• Build observability with telemetry, monitoring, tracing, and dashboards
• Develop cloud-native microservices and APIs in Python
• Automate infrastructure with Terraform and maintain CI/CD pipelines
• Mentor junior engineers and contribute to architecture reviews

• 8–10 years of software engineering experience, including 3+ years in GenAI/AI solution development
• Hands-on experience building production-grade generative AI solutions
• Expertise in Agentic AI, multi-agent architectures, tool calling, and MCP
• Strong RAG architecture experience: embeddings, vector databases, reranking, semantic search
• Hands-on with Amazon Bedrock, foundation model integration, and prompt engineering
• Deep understanding of LLMs, evaluation, model selection, and deployment patterns
• Strong Python proficiency for backend and AI application development
• Experience with AWS services (Lambda, ECS/EKS, API Gateway, S3, IAM, CloudWatch, DynamoDB, OpenSearch, Step Functions)
• Experience with Terraform and Infrastructure as Code
• Cloud-native development with Docker, Kubernetes, and CI/CD
• Knowledge of relational, NoSQL, vector, and graph databases

• LangChain, LangGraph, Semantic Kernel
• LlamaIndex
• OpenAI Agents SDK or similar agent frameworks
• Pinecone, Weaviate, Chroma, FAISS
• OpenTelemetry, Langfuse, Arize Phoenix, MLflow
• Grafana, Prometheus, CloudWatch Observability
• Neo4j and knowledge graph technologies
• MLOps, LLMOps, and AI deployment pipelines
• Fine-tuning, model evaluation, and AI benchmarking frameworks