Forward Deployment Engineer

EXLdata.ai is EXL’s flagship multi-agent orchestration platform that automates the end-to-end lifecycle of enterprise data pipelines. They are seeking a Forward Deployment Engineer who will be responsible for ensuring successful deployment and scaling of EXLdata.ai within client environments, working closely with client teams and product engineering.

Responsibilities:

• Deploy EXLdata.ai in client-owned AWS/Azure/GCP environments
• Configure networking, security, CI/CD, Kubernetes, API gateways, and identity integration
• Troubleshoot environment, infra, IAM, and pipeline-related issues
• Lead cloud-level optimizations (scaling, cost, performance tuning)
• Build, customize, and optimize data pipelines using PySpark, SQL, Databricks, Snowflake, or native hyperscaler data services
• Integrate platform agents into client workflows (Data Migration, DQ, DataOps, Annotation)
• Assist client SMEs in onboarding data sources, targets, and transformations
• Serve as the technical anchor for first-of-kind deployments at each client
• Ensure clients see measurable value from agent-driven automation (SLA reduction, pipeline acceleration, DQ uplift, migration speed)
• Provide hands-on support across discovery, configuration, runbooks, and UAT
• Work with product engineering on integrating new GenAI agents into client pipelines
• Tailor agent behaviors, triggers, and workflows for domain-specific use cases
• Share field insights that shape our agent roadmap
• Act as the “voice of the customer” for the EXLdata.ai product team
• Identify enhancements, feature gaps, and new accelerator ideas
• Participate in internal sprints, tooling improvements, and platform hardening
• Support deployments in EXL-hosted private cloud environments
• Serve as the first line of operational excellence for premium clients
• Lead operational reliability, monitoring, and support SLAs

Requirements:

• 6–12+ years as a Senior Data Engineer, Forward Deployment Engineer, or Platform Engineer
• Strong hands-on experience with at least one hyperscaler (AWS or Azure or GCP)
• Deep expertise in: PySpark, SQL, Python
• Databricks / Snowflake (one mandatory, both preferred)
• Cloud data services (Kinesis, Glue, Redshift, Synapse, BigQuery, DataProc, etc.)
• Kubernetes, Docker, CI/CD
• IAM, VPC, private networking, secrets, API management
• Demonstrated ability to work directly with client engineering teams
• Comfortable running design discussions, debugging sessions, and deployment workshops
• Strong communication skills; able to simplify technical topics for business audiences
• Ability to operate independently with a consulting mindset and ownership mentality
• Exposure to LLMs, agent tooling (LangChain, LangGraph, CrewAI, etc.), or willingness to learn fast
• Strong interest in how AI can automate data engineering and governance
• Can-do attitude; thrives in ambiguity
• Fast learner; bias for action
• Team player who collaborates across product, engineering, and client teams
• Customer-first orientation and passion for delivering measurable outcomes