Job Description
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Company Overview
A leading global professional services organization helps enterprises accelerate digital transformation through advanced engineering, data, cloud, and AI solutions. The company partners with organizations across industries to design, deploy, and scale innovative technologies that drive business value, operational efficiency, and enterprise-wide transformation. Its AI and Engineering practice focuses on delivering cutting-edge solutions powered by software engineering, data platforms, artificial intelligence, cloud infrastructure, and industry-specific expertise.
A leading organization is seeking a Lead Forward Deployed Engineer – Databricks to lead the development and deployment of enterprise-scale Generative AI solutions for strategic clients. This role combines technical leadership, client engagement, solution architecture, and hands-on engineering to help organizations transform AI initiatives into measurable business outcomes.
The successful candidate will serve as a senior practitioner and trusted advisor, leading engineering pods responsible for building production-ready AI applications. This position requires strong expertise in Databricks technologies, cloud platforms, data engineering, and GenAI solution delivery, along with the ability to engage executive stakeholders and guide complex transformation initiatives.
Key Responsibilities
Client Engagement
- Serve as the senior client-facing engineering leader and trusted advisor for product, data, and platform stakeholders.
- Lead executive-level discovery sessions and define success metrics related to quality, latency, cost, adoption, and risk.
- Develop phased implementation plans that guide projects from prototype to production and enterprise-scale deployment.
- Align executive sponsors, IT leadership, and business stakeholders around shared technical and business objectives.
- Represent the engineering capability during client pursuits, executive briefings, and strategic partner engagements.
Cross-Functional Pod Leadership & Program Governance
- Lead forward-deployed engineering pods consisting of onshore and offshore team members.
- Oversee project execution, resource management, escalations, and overall delivery health.
- Establish delivery standards, sprint cadences, stakeholder communication plans, risk management processes, and quality controls.
- Coordinate multi-pod and multi-workstream engagements to ensure architectural consistency and successful client outcomes.
- Mentor and develop engineers while fostering a culture of technical excellence and continuous improvement.
Required Qualifications
- Bachelor’s degree or equivalent in Computer Science, Data Science, Engineering, or a related field.
- 7+ years of experience in software engineering, data engineering, data science, or analytics engineering.
- 1+ years of hands-on experience building and deploying Generative AI or LLM-powered solutions in production environments.
- 1+ years of experience working with Databricks, including hands-on expertise with one or more of the following technologies:
- DBRX
- MLflow
- Vector Search
- Databricks AI Gateway
- 1+ years of experience leading project workstreams and translating business challenges into AI-driven solutions.
- Experience building reliable, maintainable, and well-documented software systems.
- Ability to travel approximately 50% based on client and project requirements.
- Eligibility to work within applicable employment authorization requirements.
Preferred Qualifications
- Experience with cloud platforms including AWS, Azure, and/or Google Cloud Platform.
- Demonstrated success working directly with client technical teams and program stakeholders in fast-paced environments.
- Data engineering experience with technologies such as Spark, Airflow, dbt, streaming architectures, and data modeling.
- Experience in machine learning, feature engineering, experimentation, or model evaluation.
- Knowledge of MLOps and LLMOps practices, including model monitoring, evaluation frameworks, and prompt management.
- Experience integrating LLM solutions with enterprise systems through APIs, microservices, or event-driven architectures.
- Experience working within hybrid onshore/offshore delivery teams.
- Familiarity with security, privacy, compliance, and governance considerations for enterprise AI systems.
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