Senior Forward Deployed AI Engineer

Accountabilities:
The Senior Forward Deployed AI Engineer will take ownership of designing, building, and deploying end-to-end AI-powered products. This role requires strong expertise across data foundations, backend systems, frontend applications, AI workflows, and cloud infrastructure, while collaborating directly with stakeholders to deliver impactful solutions. Own projects from discovery and solution design through architecture, development, deployment, and production support. Design and build scalable data foundations, including data models, schemas, dimensional models, ETL/ELT pipelines, and slowly changing dimensions. Develop full-stack applications using modern backend and frontend technologies to enable data-driven and AI-powered workflows. Build Python-based services and APIs, including scalable FastAPI applications and backend systems. Develop modern frontend experiences using frameworks such as Next.js, focusing on usability and performance. Use AI coding agents and modern AI development tools as daily productivity accelerators while maintaining engineering quality and judgment. Design and implement AI capabilities, including RAG pipelines, agentic workflows, LLM-powered applications, and AI-assisted automation. Build and optimize AI workflows using tools such as MCP servers, Skills, Plugins, and related AI orchestration technologies. Develop and manage data pipelines and workflow orchestration using technologies such as Airflow, Dagster, Prefect, Celery, or Temporal. Own CI/CD processes and cloud deployments across AWS and Azure environments. Translate complex business requirements into technical solutions and communicate trade-offs clearly with technical and non-technical stakeholders. Contribute reusable frameworks, accelerators, and engineering practices to improve delivery capabilities. Requirements: The ideal candidate is a technically strong engineer with proven production experience across data engineering, software development, and AI-powered applications. They should be comfortable owning complex projects independently and working directly with business stakeholders. 5+ years of professional experience with Python as a primary programming language for backend services, automation, and data applications. Strong experience designing and implementing data models, including dimensional modeling, normalization strategies, and enterprise data warehouse schemas. Hands-on experience building production-grade ETL/ELT pipelines with batch and incremental processing approaches. Strong knowledge of slowly changing dimensions (SCD), data transformation patterns, and data quality practices. Experience with dbt, including modular SQL transformations, testing, documentation, and incremental models. Advanced SQL skills and deep experience with modern data platforms such as Snowflake, Databricks, or similar cloud data warehouse/lakehouse technologies. Experience building APIs and backend services using FastAPI, including asynchronous design, dependency injection, and testing practices. Strong frontend development experience with modern frameworks such as Next.js, including component architecture and state management. Strong PostgreSQL experience, including schema design, optimization, and indexing strategies. Ability to design scalable systems from the ground up, including architecture decisions, service boundaries, and technical trade-offs. Experience building AI-powered applications using technologies such as RAG, LLM workflows, and agentic systems. Experience using AI development tools such as Claude Code, Cursor, or equivalent solutions as part of an engineering workflow. Experience managing cloud deployments and CI/CD pipelines on AWS or Azure. Strong communication skills with the ability to collaborate with clients and translate between business objectives and technical solutions. Experience in consulting, client delivery, forward-deployed engineering, or embedded engineering environments is a plus. Preferred qualifications: Experience with agentic AI frameworks such as LangGraph or similar technologies. Strong understanding of RAG engineering, including retrieval strategies, vector databases, chunking, reranking, and evaluation. Experience with commercial data systems, including CRM and ERP integrations. Familiarity with workflow orchestration tools such as Airflow, Dagster, Prefect, Temporal, ADF, or Databricks Workflows. Experience with streaming technologies such as Kafka, Spark Streaming, or Flink. Knowledge of vector databases such as Pinecone, Weaviate, Qdrant, or pgvector. Experience with ML experimentation and tracking platforms such as MLflow or Weights & Biases. Contributions to open-source AI/data tooling or internal engineering accelerators. Exposure to multi-cloud or hybrid cloud architectures. Benefits: Fully remote work opportunity. High-autonomy environment with ownership across the full lifecycle of complex client projects. Opportunity to work on real-world AI implementations rather than experimental prototypes. Access to modern data engineering, cloud, and AI technology stacks. Regular team offsites to collaborate, connect, and build relationships. Culture focused on innovation, ownership, and rapid execution. Opportunity to grow toward data architecture, AI engineering leadership, or platform engineering roles. Exposure to diverse industries and challenging technical problems. Opportunity to work with AI tools that enhance engineering productivity and creativity.

How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
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