AI Forward Deployment Engineer
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Job Description
AI Forward Deployment Engineer deploying EXLdata.ai solutions in client environments. Requires extensive experience in data engineering and cloud infrastructure.
Responsibilities:
- 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).
- 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.
- 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.
- Work with product engineering on integrating new GenAI agents into client pipelines.
- Tailor agent behaviors, triggers, and workflows for domain-specific use cases.
- Act as the “voice of the customer” for the EXLdata.ai product team.
- Identify enhancements, feature gaps, and new accelerator ideas.
Requirements:
- 6–12+ years as a Senior Data Engineer, Forward Deployment 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
- 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.
- 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.
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