Forward Deployment Engineer - Senior Manager C2

Gurugram, IndiaPosted Aug 6, 2026

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.

Responsibilities for Internal Candidates
Key Responsibilities

1. Deployment & Infrastructure Engineering

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.

Qualifications for Internal Candidates
Technical Expertise

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

Same Posting Description for Internal and External Candidates