Forward Deployment Engineer
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**Job Description: 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**
* 12\+ years as a **Senior Data Engineer / Architect** , 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: 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.
**Qualifications: Technical Expertise**
* 12\+ years as a **Senior Data Engineer / Architect** , 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.
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