Forward Deployment Engineer

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    "ShortDescriptionStr": "Forward Deployment Engineer – EXLdata.ai (Senior Data Engineer / Platform Orchestrator)\nLocation: US / Remote\nPractice: EXLdata.ai – Multi-Agent Data Engineering Platform\nRole Type: Client-Facing | High-Visibility | Strategic Delivery\n",
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    "ExternalDescriptionStr": "<p><strong>About EXLdata.ai</strong></p>\n<p>EXLdata.ai is EXL’s flagship&nbsp;<strong>multi-agent orchestration platform</strong>&nbsp;that automates the&nbsp;<strong>end-to-end lifecycle of enterprise data pipelines</strong>&nbsp;– spanning&nbsp;<strong>data migration, data engineering, data quality, data governance, data ops</strong>, and&nbsp;<strong>unstructured data annotation</strong>.<br>\n 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:</p>\n<ol>\n <li><strong>Client-Deployed Model:</strong>&nbsp;Deploy EXLdata.ai within the client’s own cloud (AWS/Azure/GCP) and orchestrate their pipelines using our accelerator suite.</li>\n <li><strong>Managed Platform Model:</strong>&nbsp;EXL hosts EXLdata.ai in a dedicated private cloud and provides a&nbsp;<strong>white-glove, Data-Product-as-a-Service</strong>&nbsp;experience.</li>\n</ol>\n<p>To support this growth, we are hiring a&nbsp;<strong>Forward Deployment Engineer</strong>&nbsp;who will serve as the technical anchor at the intersection of&nbsp;<strong>cloud engineering, data engineering, GenAI, and client value realization</strong>.</p>\n<div>\n &nbsp;\n</div>\n<p><strong>Role Overview</strong></p>\n<p>The Forward Deployment Engineer (FDE) is the&nbsp;<strong>tip-of-the-spear</strong>&nbsp;technical role responsible for ensuring that EXLdata.ai is&nbsp;<strong>successfully deployed, adopted, and scaled</strong>&nbsp;within client environments.</p>\n<p>You will work side-by-side with client teams, product engineering, and our GenAI agent teams to:</p>\n<ul>\n <li><strong>Deploy EXLdata.ai</strong>&nbsp;into client cloud infrastructure (AWS/Azure/GCP)</li>\n <li><strong>Resolve infra, security, and data pipeline issues</strong>&nbsp;in real time</li>\n <li><strong>Customize accelerators and agent workflows</strong>&nbsp;for client-specific needs</li>\n <li><strong>Drive measurable value realization</strong>&nbsp;from Day 1</li>\n <li><strong>Champion product enhancements back to the EXLdata.ai engineering team</strong></li>\n <li><strong>Deliver white-glove support</strong>&nbsp;for clients using our managed platform offering</li>\n</ul>\n<p>This role is ideal for a&nbsp;<strong>senior data engineer</strong>&nbsp;who loves solving real-world problems, can operate in ambiguous conditions, learns fast, and thrives in high-impact client environments.</p>",
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    "CorporateDescriptionStr": "<div>\n EXL (NASDAQ: EXLS) is a leading data analytics and digital operations and solutions company. We partner with clients using a data and AI-led approach to reinvent business models, drive better business outcomes and unlock growth with speed. EXL harnesses the power of data, analytics, AI, and deep industry knowledge to transform operations for the world’s leading corporations in industries including insurance, healthcare, banking and financial services, media and retail, among others. EXL was founded in 1999 with the core values of innovation, collaboration, excellence, integrity and respect. We are headquartered in New York and have more than 54,000 employees spanning six continents. For more information, visit&nbsp;<a href=\"http://www.exlservice.com/\" target=\"_blank\" rel=\"nofollow\">www.exlservice.com</a>.\n</div>\n<div>\n <div>\n  <p class=\"MsoNormal\"><br/></p>\n </div><i>EXL never requires or asks for fees/payments or credit card or bank details during any phase of the recruitment or hiring process and has not authorized any agencies or partners to collect any fee or payment from prospective candidates. EXL will only extend a job offer after a candidate has gone through a formal interview process with members of EXL’s Human Resources team, as well as our hiring managers.</i>\n</div>\n<div>\n</div>",
    "ExternalPostedStartDate": "2026-06-03T19:46:32+00:00",
    "ExternalQualificationsStr": "<p><strong>Required Skills &amp; Experience</strong></p><p><strong>Technical Expertise</strong></p><ul><li>6–12+ years as a <strong>Senior Data Engineer</strong>, Forward Deployment Engineer, or Platform Engineer.</li><li>Strong hands-on experience with <strong>at least one hyperscaler</strong> (AWS or Azure or GCP).</li><li>Deep expertise in:<ul><li><strong>PySpark</strong>, SQL, Python</li><li><strong>Databricks / Snowflake</strong> (one mandatory, both preferred)</li><li>Cloud data services (Kinesis, Glue, Redshift, Synapse, BigQuery, DataProc, etc.)</li><li>Kubernetes, Docker, CI/CD</li><li>IAM, VPC, private networking, secrets, API management</li></ul></li></ul><p><strong>Delivery &amp; Client Facing Skills</strong></p><ul><li>Demonstrated ability to <strong>work directly with client engineering teams</strong>.</li><li>Comfortable running design discussions, debugging sessions, and deployment workshops.</li><li>Strong communication skills; able to simplify technical topics for business audiences.</li><li>Ability to operate independently with a <strong>consulting mindset and ownership mentality</strong>.</li></ul><p><strong>GenAI &amp; Multi-Agent Curiosity</strong></p><ul><li>Exposure to LLMs, agent tooling (LangChain, LangGraph, CrewAI, etc.), or willingness to learn fast.</li><li>Strong interest in how AI can automate data engineering and governance.</li></ul><p><strong>Mindset &amp; Attributes</strong></p><ul><li>“Can-do” attitude; thrives in ambiguity.</li><li>Fast learner; bias for action.</li><li>Team player who collaborates across product, engineering, and client teams.</li></ul><p>Customer-first orientation and passion for delivering measurable outcomes.<br /><br /><br />The approximate salary range for this role is $130-150k.<br /><br />The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.</p>",
    "InternalQualificationsStr": "<p><strong>Required Skills &amp; Experience</strong></p><p><strong>Technical Expertise</strong></p><ul><li>6–12+ years as a <strong>Senior Data Engineer</strong>, Forward Deployment Engineer, or Platform Engineer.</li><li>Strong hands-on experience with <strong>at least one hyperscaler</strong> (AWS or Azure or GCP).</li><li>Deep expertise in:<ul><li><strong>PySpark</strong>, SQL, Python</li><li><strong>Databricks / Snowflake</strong> (one mandatory, both preferred)</li><li>Cloud data services (Kinesis, Glue, Redshift, Synapse, BigQuery, DataProc, etc.)</li><li>Kubernetes, Docker, CI/CD</li><li>IAM, VPC, private networking, secrets, API management</li></ul></li></ul><p><strong>Delivery &amp; Client Facing Skills</strong></p><ul><li>Demonstrated ability to <strong>work directly with client engineering teams</strong>.</li><li>Comfortable running design discussions, debugging sessions, and deployment workshops.</li><li>Strong communication skills; able to simplify technical topics for business audiences.</li><li>Ability to operate independently with a <strong>consulting mindset and ownership mentality</strong>.</li></ul><p><strong>GenAI &amp; Multi-Agent Curiosity</strong></p><ul><li>Exposure to LLMs, agent tooling (LangChain, LangGraph, CrewAI, etc.), or willingness to learn fast.</li><li>Strong interest in how AI can automate data engineering and governance.</li></ul><p><strong>Mindset &amp; Attributes</strong></p><ul><li>“Can-do” attitude; thrives in ambiguity.</li><li>Fast learner; bias for action.</li><li>Team player who collaborates across product, engineering, and client teams.</li></ul><p>Customer-first orientation and passion for delivering measurable outcomes.<br /><br /> </p>",
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    "ExternalResponsibilitiesStr": "<p><strong>Key Responsibilities</strong></p><p><strong>1. Deployment &amp; Infrastructure Engineering</strong></p><ul><li>Deploy EXLdata.ai in client-owned AWS/Azure/GCP environments.</li><li>Configure networking, security, CI/CD, Kubernetes, API gateways, and identity integration.</li><li>Troubleshoot environment, infra, IAM, and pipeline-related issues.</li><li>Lead cloud-level optimizations (scaling, cost, performance tuning).</li></ul><p><strong>2. Data Engineering &amp; Pipeline Enablement</strong></p><ul><li>Build, customize, and optimize data pipelines using <strong>PySpark, SQL, Databricks, Snowflake</strong>, or native hyperscaler data services.</li><li>Integrate platform agents into client workflows (Data Migration, DQ, DataOps, Annotation).</li><li>Assist client SMEs in onboarding data sources, targets, and transformations.</li></ul><p><strong>3. Value Realization &amp; Client Enablement</strong></p><ul><li>Serve as the <strong>technical anchor</strong> for first-of-kind deployments at each client.</li><li>Ensure clients see measurable value from agent-driven automation (SLA reduction, pipeline acceleration, DQ uplift, migration speed).</li><li>Provide hands-on support across discovery, configuration, runbooks, and UAT.</li></ul><p><strong>4. GenAI Agent Integration</strong></p><ul><li>Work with product engineering on integrating new GenAI agents into client pipelines.</li><li>Tailor agent behaviors, triggers, and workflows for domain-specific use cases.</li><li>Share field insights that shape our agent roadmap.</li></ul><p><strong>5. Product Innovation &amp; Feedback Loop</strong></p><ul><li>Act as the “voice of the customer” for the EXLdata.ai product team.</li><li>Identify enhancements, feature gaps, and new accelerator ideas.</li><li>Participate in internal sprints, tooling improvements, and platform hardening.</li></ul><p><strong>6. Managed Service / White-Glove Model</strong></p><ul><li>Support deployments in EXL-hosted private cloud environments.</li><li>Serve as the first line of operational excellence for premium clients.</li><li>Lead operational reliability, monitoring, and support SLAs.</li></ul>",
    "InternalResponsibilitiesStr": "<p><strong>Key Responsibilities</strong></p><p><strong>1. Deployment &amp; Infrastructure Engineering</strong></p><ul><li>Deploy EXLdata.ai in client-owned AWS/Azure/GCP environments.</li><li>Configure networking, security, CI/CD, Kubernetes, API gateways, and identity integration.</li><li>Troubleshoot environment, infra, IAM, and pipeline-related issues.</li><li>Lead cloud-level optimizations (scaling, cost, performance tuning).</li></ul><p><strong>2. Data Engineering &amp; Pipeline Enablement</strong></p><ul><li>Build, customize, and optimize data pipelines using <strong>PySpark, SQL, Databricks, Snowflake</strong>, or native hyperscaler data services.</li><li>Integrate platform agents into client workflows (Data Migration, DQ, DataOps, Annotation).</li><li>Assist client SMEs in onboarding data sources, targets, and transformations.</li></ul><p><strong>3. Value Realization &amp; Client Enablement</strong></p><ul><li>Serve as the <strong>technical anchor</strong> for first-of-kind deployments at each client.</li><li>Ensure clients see measurable value from agent-driven automation (SLA reduction, pipeline acceleration, DQ uplift, migration speed).</li><li>Provide hands-on support across discovery, configuration, runbooks, and UAT.</li></ul><p><strong>4. GenAI Agent Integration</strong></p><ul><li>Work with product engineering on integrating new GenAI agents into client pipelines.</li><li>Tailor agent behaviors, triggers, and workflows for domain-specific use cases.</li><li>Share field insights that shape our agent roadmap.</li></ul><p><strong>5. Product Innovation &amp; Feedback Loop</strong></p><ul><li>Act as the “voice of the customer” for the EXLdata.ai product team.</li><li>Identify enhancements, feature gaps, and new accelerator ideas.</li><li>Participate in internal sprints, tooling improvements, and platform hardening.</li></ul><p><strong>6. Managed Service / White-Glove Model</strong></p><ul><li>Support deployments in EXL-hosted private cloud environments.</li><li>Serve as the first line of operational excellence for premium clients.</li><li>Lead operational reliability, monitoring, and support SLAs.</li></ul>",
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