Forward Deployment Engineer

Forward Deployment Engineer – EXLdata.ai (Senior Data Engineer / Platform Orchestrator)

Location - All exl Location
Practice:  EXLdata.ai – Multi-Agent Data Engineering Platform
Role Type:  Client-Facing | High-Visibility | Strategic Delivery

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About EXLdata.ai

EXLdata.ai is EXL’s flagship  multi-agent orchestration platform  that automates the  end-to-end lifecycle of enterprise data pipelines  – spanning  data migration, data engineering, data quality, data governance, data ops , and  unstructured data annotation .
Having launched our MVP and now expanding across multiple Fortune-500 engagements, we are entering a rapid scale-up phase with two distinct delivery models:

1. Client-Deployed Model:  Deploy EXLdata.ai within the client’s own cloud (AWS/Azure/GCP) and orchestrate their pipelines using our accelerator suite.
2. Managed Platform Model:  EXL hosts EXLdata.ai in a dedicated private cloud and provides a  white-glove, Data-Product-as-a-Service  experience.

To support this growth, we are hiring a  Forward Deployment Engineer  who will serve as the technical anchor at the intersection of  cloud engineering, data engineering, GenAI, and client value realization .

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Role Overview

The Forward Deployment Engineer (FDE) is the  tip-of-the-spear  technical role responsible for ensuring that EXLdata.ai is  successfully deployed, adopted, and scaled  within client environments.

You will work side-by-side with client teams, product engineering, and our GenAI agent teams to:

• Deploy EXLdata.ai  into client cloud infrastructure (AWS/Azure/GCP)
• Resolve infra, security, and data pipeline issues  in real time
• Customize accelerators and agent workflows  for client-specific needs
• Drive measurable value realization  from Day 1
• Champion product enhancements back to the EXLdata.ai engineering team
• Deliver white-glove support  for clients using our managed platform offering

This role is ideal for a  senior data engineer  who loves solving real-world problems, can operate in ambiguous conditions, learns fast, and thrives in high-impact client environments.

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Key Responsibilities

1. Deployment & Infrastructure Engineering

• 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).

2. Data Engineering & Pipeline Enablement

• 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.

3. Value Realization & Client Enablement

• 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.

4. GenAI Agent Integration

• 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.

5. Product Innovation & Feedback Loop

• 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.

6. Managed Service / White-Glove Model

• 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.

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Required Skills & Experience

Technical Expertise

• 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

Delivery & Client Facing Skills

• 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 .

GenAI & Multi-Agent Curiosity

• 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.

Mindset & Attributes

• “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.