About the role

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

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

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:

Client-Deployed Model: Deploy EXLdata.ai within the client’s own cloud (AWS/Azure/GCP) and orchestrate their pipelines using our accelerator suite.
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 .

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.

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.

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.