AI Engineer - FDE (Forward Deployed Engineer)

Databricks interviews typically involve three rounds: a technical coding round, a system design or case study round focusing on AI solutions, and an HR round. The difficulty is moderate to high, with emphasis on problem‑solving, AI knowledge, and communication skills. Candidates are advised to practice coding on platforms like LeetCode and review recent GenAI research papers.

Databricks fosters a collaborative, fast‑paced environment where employees are encouraged to experiment and innovate. The company offers ample growth opportunities, continuous learning resources, and a healthy work‑life balance with flexible remote work options.

Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, Operations Research, or a related quantitative field. Minimum 60% (or 6.5 CGPA) in the qualifying exam. No backlogs allowed. Strong academic record and relevant project or internship experience in AI/ML are preferred.

Round 1: Technical interview covering coding, data structures, and AI concepts → Round 2: System design & case study with a focus on GenAI deployment → Round 3: HR & cultural fit interview

Databricks is a leading Data and AI company that powers more than 20,000 organizations worldwide, including Fortune 500 firms such as adidas, AT&T, and Mastercard. The company offers a unified platform that integrates data engineering, data science, and machine learning into a single Lakehouse architecture, enabling customers to build, deploy, and scale AI applications at scale. With a strong focus on innovation, Databricks has pioneered technologies such as Spark, Delta Lake, and GenAI solutions that are now industry standards. The AI Forward Deployed Engineering (AI FDE) team is a highly specialized, customer‑facing group that delivers professional services engagements. As an AI Engineer – FDE, you will work directly with clients to design, prototype, and production‑grade deploy cutting‑edge Generative AI solutions. Your role will involve building RAG pipelines, multi‑agent systems, Text2SQL interfaces, and fine‑tuning large language models using tools like HuggingFace, LangChain, and DSPy. You will also be responsible for evaluating model performance, optimizing inference latency, and ensuring robust deployment on cloud platforms such as AWS, Azure, or GCP. Key responsibilities include: 1. Collaborating with product and engineering teams to translate customer requirements into technical solutions. 2. Designing and implementing end‑to‑end GenAI pipelines that integrate data ingestion, model training, and inference. 3. Deploying production‑grade AI services on Databricks’ cloud platform and managing scalability. 4. Conducting model evaluation, bias assessment, and performance tuning. 5. Presenting findings and demos to technical and non‑technical stakeholders. 6. Writing clean, maintainable code in Python and SQL. 7. Mentoring junior team members and sharing best practices. 8. Participating in client workshops and conferences such as Data + AI Summit. 9. Contributing to internal knowledge bases and open‑source projects. 10. Staying current with the latest research in GenAI, LLMOps, and ML. Tech stack: Python, PyTorch, HuggingFace, LangChain, DSPy, pandas, scikit‑learn, Apache Spark, Databricks, AWS/Azure/GCP, SQL, Docker, Kubernetes. Growth path: Starting as a Forward Deployed Engineer, you can progress to Senior FDE, Lead FDE, or move into product management or research roles within Databricks. The company encourages continuous learning, offers access to internal training, and rewards innovation. Why join? Databricks offers a collaborative culture that values curiosity, experimentation, and impact. Employees enjoy competitive compensation, flexible remote work, and opportunities to work on high‑impact projects that shape the future of AI. The company’s commitment to diversity, inclusion, and employee well‑being makes it an attractive place for fresh talent to grow.