Senior Forward Deployed AI Engineer (Python, Data & GenAI)

Senior Forward Deployed AI Engineer (Python Data & GenAI)

The Opportunity

We are looking for a senior Forward Deployed Engineer who combines production-grade software engineering data engineering AI application development and client-facing consulting. You will work closely with business users subject matter experts product owners and engineering teams to understand complex workflows rapidly validate solutions using real data remove technical blockers and own delivery through production launch stabilization adoption and handover.

What You Will Do

• Work with subject matter experts product owners business users and engineers to map workflows users data constraints and decision points.
  

• Agree on the first useful release measurable success criteria and a practical roadmap from prototype to production.
  

• Profile unfamiliar data using Python pandas PySpark and SQL to assess coverage quality volumes distributions edge cases and feasibility.
  

• Build working solutions using real data and APIs validate them with users and iteratively harden them for production.
  

• Design develop and deploy enterprise AI and data applications using Python PySpark SQL REST APIs FastAPI and cloud data platforms.
  

• Build Claude-powered applications copilots and AI workflows using prompt engineering RAG vector databases semantic search and agentic AI patterns.
  

• Design batch real-time and streaming data pipelines and integrations across enterprise systems.
  

• Implement MLOps/LLMOps capabilities including lifecycle management CI/CD MLflow monitoring deployment automation rollback and production support.
  

• Create evaluation datasets with subject matter experts measure model and LLM quality perform structured error analysis and prioritize improvements.
  

• Resolve data-access API identity network security infrastructure and deployment blockers with technical teams.
  

• Own post-go-live monitoring incident diagnosis rollback stabilization latency performance reliability and cost optimization.
  

• Measure adoption and business impact using agreed KPIs then complete structured handover with runbooks support ownership and knowledge transfer.
  

What You Bring

• 10 years of experience in data engineering AI application development cloud data platforms and production software engineering.
  

• Strong hands-on expertise in Python PySpark SQL REST APIs FastAPI Git Docker Kubernetes Airflow MLflow CI/CD and distributed processing.
  

• Experience with Claude prompt engineering RAG vector databases semantic search and agentic AI patterns.
  

• Strong knowledge of ETL/ELT batch and streaming pipelines data quality metadata lineage observability and DataOps.
  

• Experience operationalizing MLOps/LLMOps including model lifecycle management automated deployment monitoring rollback and production support.
  

• Knowledge of enterprise cloud services across compute storage integration orchestration networking monitoring security and cost optimization.
  

• Understanding of IAM/RBAC encryption secrets management privacy controls auditability compliance and secure data access.
  

• Ability to translate business requirements into scalable designs define non-functional requirements estimate effort and drive implementation.
  

• Financial services experience and strong client-facing skills across discovery rapid prototyping solution shaping workshops and technical presentations.
  

• Bachelors degree in a relevant discipline. Cloud Kubernetes AI engineering MLOps data engineering architecture or security certifications are preferred.