Forward-Deployed AI Engineer

NodixAI is looking for experienced Forward-Deployed AI Engineers (FDEs) to work directly with enterprise customers on production AI implementations. This is a fractional / project-based opportunity for experienced engineers who can contribute to client engagements as needed. This is a hands-on engineering role. You’ll work closely with our Principal Architect and customer teams to turn enterprise AI architectures into working production systems. RESPONSIBILITIES - Build and deploy RAG systems, enterprise knowledge applications, agentic workflows, and AI-enabled applications. - Integrate AI systems with enterprise APIs, databases, SaaS applications, data platforms, and operational workflows. - Build data ingestion and processing pipelines for structured and unstructured enterprise data. - Implement tool/API integrations, workflow orchestration, human approvals, evaluation, guardrails, and observability. - Deploy services into AWS, Azure, or GCP environments, including private/VPC-based architectures where required. - Implement authentication, authorization, secrets management, logging, and security controls. - Work directly with customer engineering and technology teams to troubleshoot integrations and adapt solutions to real enterprise environments. - Support testing, production rollout, monitoring, reliability, and ongoing improvements. REQUIRED TECHNICAL EXPERIENCE - Strong hands-on Python development experience. - Strong backend and API development experience using frameworks such as FastAPI, Flask, Django, or equivalent. - Production experience building GenAI / LLM applications using major model APIs or platforms such as OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, or Google Vertex AI. - Hands-on experience building RAG systems, including document ingestion, chunking, embeddings, retrieval, reranking, and vector/search infrastructure. - Experience with agentic systems, tool calling, structured outputs, multi-step workflows, and human-in-the-loop patterns. - Experience with frameworks or orchestration tools such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent. - Experience with vector and search technologies such as Pinecone, Weaviate, Qdrant, Elasticsearch/OpenSearch, pgvector, or equivalent. - Experience working with relational databases such as PostgreSQL and integrating with enterprise data sources. - Experience integrating REST APIs, webhooks, third-party SaaS systems, and enterprise applications. - Working knowledge of at least one major cloud platform: AWS, Azure, or GCP. - Experience with Docker, CI/CD, Git, infrastructure configuration, logging, monitoring, and production deployment. - Understanding of IAM, OAuth/OIDC, secrets management, private networking, encryption, and enterprise security patterns. - Experience implementing LLM evaluation, tracing, observability, guardrails, and production monitoring. STRONG PLUS - Kubernetes - Terraform or other Infrastructure as Code tooling - Snowflake or Databricks - Kafka or other event-driven systems - CDC and data synchronization - Redis - SQL and data engineering pipelines - MCP and modern agent/tool integration patterns - Enterprise SaaS integrations - Experience deploying AI systems inside customer-controlled cloud environments QUALIFICATIONS - Strong hands-on software engineering background. - Recent experience taking GenAI / LLM applications into production. - Experience working directly with customers or enterprise engineering teams. - Able to debug across application, AI, data, integration, and cloud layers. - Comfortable working in environments where requirements evolve during implementation. - Able to operate independently while collaborating closely with architects and customer teams. INTERESTED? Please send: 1. A brief overview of your technical background and availability. 2. One or two examples of enterprise AI systems you personally built or helped deploy into production. 3. For each example, briefly describe the technologies you used and what you personally implemented. NodixAI: https://nodixai.co