Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading global professional services organization is expanding its AI & Engineering practice to help enterprises accelerate AI adoption and digital transformation. The organization delivers cutting-edge solutions across artificial intelligence, data, software engineering, cloud infrastructure, and analytics. By combining industry expertise with advanced engineering capabilities, the team enables clients to modernize technology platforms, optimize business operations, and achieve measurable outcomes through innovative AI-powered solutions.
The organization is seeking a Forward Deployed Engineer – Databricks to develop and deliver enterprise-scale Generative AI solutions for clients across multiple industries. This role combines hands-on software engineering, AI solution development, and client engagement, requiring close collaboration with technical teams and business stakeholders to rapidly prototype, build, and deploy production-ready AI applications.
The successful candidate will work within a multidisciplinary engineering team to translate business challenges into scalable AI-powered solutions while leveraging Databricks technologies, modern software engineering practices, and cloud platforms. This position offers the opportunity to contribute to high-impact AI transformation initiatives while mentoring team members and driving technical excellence.
Key Responsibilities
- Partner directly with clients to identify business needs and transform high-value Generative AI use cases into practical solutions.
- Collaborate with product owners, architects, engineers, and business leaders to align project priorities and delivery objectives.
- Lead discovery sessions, technical workshops, and solution design discussions with client stakeholders.
- Rapidly prototype, develop, and deploy AI-powered applications that deliver measurable business value.
- Build scalable AI-enabled platforms, agentic workflows, and enterprise AI solutions using Databricks technologies.
- Develop reusable engineering assets, including code libraries, prompt templates, runbooks, and reference implementations.
- Apply software engineering best practices, including testing, CI/CD, logging, documentation, version control, and code quality standards.
- Design AI architectures that balance performance, security, scalability, cost, latency, and model governance.
- Support sprint planning, solution estimation, and technical implementation activities.
- Mentor junior engineers while contributing independently within cross-functional engineering teams.
Required Qualifications
- Bachelor’s degree (or equivalent) in Computer Science, Data Science, Engineering, or a related field.
- 3+ 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 or client environments.
- 1+ year of experience using Databricks and technologies such as DBRX, MLflow, Vector Search, or Databricks AI Gateway.
- Experience translating business requirements into scalable AI solutions.
- Experience building reliable, maintainable, and well-documented production software.
- Strong communication and collaboration skills with the ability to work directly with technical and business stakeholders.
- Willingness to travel approximately 50% based on project requirements.
- Limited immigration sponsorship may be available.
Preferred Qualifications
- Experience working with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
- Experience collaborating directly with client engineering teams in fast-paced consulting or delivery environments.
- Knowledge of data engineering technologies including Apache Spark, Airflow, dbt, streaming platforms, data modeling, or machine learning workflows.
- Experience with MLOps or LLMOps practices, including model evaluation, monitoring, prompt management, and deployment pipelines.
- Experience integrating Large Language Model solutions with enterprise applications through APIs, microservices, or event-driven architectures.
- Experience working within hybrid onshore and offshore delivery teams.
- Familiarity with enterprise security, privacy, governance, and compliance requirements.
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