Job Description
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Company Overview
A global professional services organization is helping enterprises accelerate AI adoption by delivering innovative engineering, data, cloud, and artificial intelligence solutions. The company partners with organizations across industries to design, deploy, and scale enterprise-grade AI applications, enabling clients to modernize technology platforms and drive business transformation through cutting-edge engineering capabilities.
The organization is seeking a Lead Forward Deployed Engineer – Databricks to lead engineering teams that develop and deploy Generative AI solutions for enterprise clients. This role combines hands-on technical leadership with strategic client engagement, guiding AI initiatives from discovery through production while ensuring scalable, secure, and high-quality solution delivery.
Key Responsibilities
- Lead forward-deployed engineering teams in designing, developing, and deploying enterprise-scale Generative AI solutions.
- Serve as the senior client-facing engineering leader, building trusted relationships with product, data, and platform stakeholders.
- Lead executive discovery sessions, define project success metrics, and develop phased implementation roadmaps from prototype to production.
- Guide technical decision-making while communicating engineering trade-offs to executive stakeholders.
- Represent the engineering organization during client engagements, executive briefings, and strategic initiatives.
- Lead engineering pods, oversee delivery execution, resource planning, risk management, and project health.
- Establish delivery standards, sprint planning processes, quality controls, and stakeholder communication practices.
- Coordinate multiple engineering workstreams while maintaining architectural consistency and delivery excellence.
- Mentor and develop engineers through technical coaching and leadership.
- Architect and oversee the delivery of LLM-powered applications, including AI copilots, intelligent assistants, agentic workflows, and knowledge search solutions.
- Define best practices for prompt engineering, AI tool orchestration, and human-in-the-loop workflows.
- Design and govern Retrieval-Augmented Generation (RAG) pipelines, including data ingestion, embedding, vector retrieval, chunking, and hybrid search architectures.
- Establish evaluation frameworks covering model quality, safety, hallucination mitigation, latency, governance, and operational cost.
- Review production-quality code and contribute to software development when required.
- Guide the architecture of scalable data pipelines supporting AI and analytics workloads.
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+ year of hands-on experience developing and deploying Generative AI or Large Language Model (LLM) solutions in production environments.
- 1+ year of experience working with Databricks and technologies such as DBRX, MLflow, Vector Search, or Databricks AI Gateway.
- Experience leading technical projects and translating business requirements into AI-driven solutions.
- Experience developing reliable, maintainable, and well-documented production software.
- Strong leadership and client engagement skills with the ability to influence technical and business stakeholders.
- Willingness to travel up to 50% based on project requirements.
- Eligibility to work in environments where limited immigration sponsorship may be available.
Preferred Qualifications
- Experience with cloud platforms including AWS, Microsoft Azure, or Google Cloud Platform.
- Experience collaborating directly with client engineering teams in fast-paced enterprise environments.
- Knowledge of data engineering technologies such as Apache Spark, Airflow, dbt, streaming platforms, or machine learning workflows.
- Experience implementing MLOps or LLMOps practices, including model monitoring, evaluation frameworks, and prompt lifecycle management.
- Experience integrating AI applications with enterprise systems through APIs, microservices, or event-driven architectures.
- Experience working within hybrid onshore and offshore engineering teams.
- Familiarity with enterprise security, privacy, governance, and compliance best practices.
Disclaimer
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