Lead client deployments of EXLdata.ai across AWS/Azure/GCP, build and optimize PySpark/SQL data pipelines (Databricks/Snowflake), configure cloud infra (Kubernetes, CI/CD, networking, IAM), integrate GenAI agents, and act as the technical lead for client enablement, operational reliability, and product feedback.
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.
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.
EXL Faridabad, Haryana, IND Office
Faridabad, India
EXL Ghāziābād, Uttar Pradesh, IND Office
Ghāziābād, India
EXL Greater Noida, Uttar Pradesh, IND Office
Greater Noida, India
EXL Gurugram, Haryana, IND Office
Gurugram, India
EXL New Delhi, Delhi, IND Office
New Delhi, India
EXL Noida, Uttar Pradesh, IND Office
Noida, India
Similar Jobs
.jpeg)
Artificial Intelligence • Hardware • Information Technology • Machine Learning
The Sr. Manager for CAT Assembly Manufacturing at Micron Technology oversees manufacturing methods, personnel management, and operational performance to meet business objectives. Responsibilities include staff recruitment, process optimization, and continuous improvement efforts while maintaining quality standards and budget control.
Cloud • Information Technology • Productivity • Software • Automation
As a Product Support Sr. Engineer, you will troubleshoot complex technical issues, work with the Boomi AtomSphere Platform, and ensure customer success across various global regions.
Top Skills:
Boomi Atomsphere PlatformCharles ProxyEltETLGroovyHadoopHttp/SJavaJavaScriptKubernetesLinux OsNetSuiteOauth 2.0PostmanRancher DesktopReactRestSalesforceSftpSoapSsl/TlsTcp/IpWindows OsWiresharkWsdl
Cloud • Information Technology • Security • Software
Lead quality efforts for major product areas, define test strategies, write and maintain complex automated tests, enable developers to shift testing left, participate in incident reviews and root-cause analysis, mentor junior QEs, and ensure releases meet reliability, performance, and security standards while partnering with architects and development leads.
Top Skills:
Ci/CdJavaScriptPlaywrightPytestPythonTypescript
What you need to know about the Delhi Tech Scene
Delhi, India's capital city, is a place where tradition and progress co-exist. While Old Delhi is known for its rich history and bustling markets, New Delhi is defined by its modern architecture. It's clear the region places a strong emphasis on preserving its cultural heritage while embracing technological advancements, particularly in artificial intelligence, which plays a central role in shaping the city's tech landscape, fueled by investments in research and development.
