Forward Deployed Engineer at Databricks

A Forward Deployed Engineer at Databricks in India is a senior, customer-embedded engineer who builds data and GenAI systems on the Databricks platform for strategic customers and takes them into production. Databricks has posted FDE roles for Bengaluru and remote India at several levels, from AI Engineer - FDE to Staff FDE, plus a Bengaluru-based manager of the team. Every India listing asks for at least five years of experience, so this is not an entry-level route.

The roles sit inside a growing India operation. In April 2025, Outlook Business reported that Databricks planned to invest more than $250 million in India over three years and grow its India headcount by over 50% to more than 750 people, with an R&D centre in Bengaluru.

What the FDE does at Databricks

Databricks uses the FDE title across two kinds of work, and the India listings show both.

GenAI delivery (AI Engineer - FDE). The AI Engineer - FDE listing for India places the role in the AI Forward Deployed Engineering team, a customer-facing professional services group. The engineer:

• builds GenAI solutions using techniques from Databricks’ Mosaic AI research;
• owns production rollouts of customer-facing and internal GenAI applications, including evaluation and optimisation;
• acts as a technical adviser to customers across industries;
• feeds what they learn back to product and engineering to shape the roadmap.

Data platform delivery (Senior and Staff FDE). The Sr FDE role for remote India is closer to a hands-on solutions architect: designing customer solutions on Databricks, leading architecture decisions, integrating with customer systems and building reusable assets for many accounts, with about 20% travel. The Staff FDE listing adds migrations from third-party platforms, reference architectures and production tooling, and two years of RAG, MCP or context-engineering work.

Exponent’s Databricks FDE guide says the FDE organisation was formalised in June 2026 and describes typical work as ETL pipelines and data models, application development, and RAG and agent delivery on tight client timelines.

Experience and skills Databricks asks for

Sources: the Databricks careers page for the manager role and the job-board copies linked above for the others.

Two practical notes. First, the GenAI-focused listing also asks for a graduate degree in a quantitative field or equivalent practical experience, and preferred experience with the Databricks platform and Spark. Second, postings move: Built In Bengaluru marked one AI Engineer - FDE copy as removed on 5 October 2026, while Indeed India still showed 8 Databricks FDE-related openings, including Bengaluru and remote India roles and a Deployment Strategist role for India, in November 2026. PitchMeAI flagged the Sr FDE remote India listing as open for over 80 days. Always confirm a role on Databricks’ own careers site before applying.

Pay

None of the India FDE listings show pay. The closest public figure is AmbitionBox, where Databricks Solution Architects in India self-report ₹32–54.5 LPA for 5–18 years of experience. That is based on only 9 salaries, marked low confidence and last updated in February 2025, so treat it as a rough floor for comparable customer-facing technical roles, not as FDE pay.

For context only, Exponent lists US base salaries of about $181k–$248k for Senior FDE postings from July 2026, and notes total compensation including equity is not published. US bands don’t convert to Indian pay. For a wider view of how global platform companies pay FDEs in India, use our FDE Salary Estimator.

How to prepare

1. Get fluent on the Databricks platform itself. Build a small lakehouse project: ingest raw files, transform them with Spark, model the tables and query them in SQL. Every India listing names Spark or the Databricks platform.
2. Ship one GenAI app with evaluation. A RAG or Text2SQL assistant over real data, with an evaluation set measuring accuracy and latency. The AI Engineer - FDE role asks specifically for production GenAI with evaluation and optimisation.
3. Practise notebook coding. Exponent reports a practical easy-to-medium coding round in a notebook. Rehearse clean Python with dictionaries, strings and lists while explaining your choices aloud.
4. Rehearse business decomposition. Take a vague goal such as “reduce loan default for a bank” and turn it into data sources, a pipeline, a model and a KPI. Ask clarifying questions first.
5. Bring customer delivery stories. Prepare two or three examples of owning a deployment for a client: a migration, a tough stakeholder, a slipped deadline. The AI Engineer - FDE listing prefers consulting experience, so Databricks delivery work at a consulting or IT services firm maps directly onto these roles.

For more on the round types, see our FDE interview questions guide, compare with Google’s FDE role, and check which skills you need to build with the FDE Alignment Checker.

Databricks FDE interview: what is publicly known

1. Recruiter screen: About 30 minutes on background, motivation and logistics, according to Exponent's Databricks FDE guide. It is non-technical. This is a reported pattern, not an official India process.
2. Technical screen: 45–60 minutes of short questions on SQL, data manipulation, light design and high-level AI concepts, testing breadth and practical judgment (Exponent).
3. Coding: About an hour writing a practical easy-to-medium function in a notebook, mostly with dictionaries, strings and lists. Clean structure and thinking aloud matter (Exponent).
4. Decomposition: About an hour turning a vague business goal into a system design: clarify constraints, tie the work to a KPI and an end user, and prioritise (Exponent).
5. Leadership and values: A conversation on ownership, delivery and judgment mapped to Databricks' core values; Exponent reports final decisions go to a hiring committee. Some teams add a take-home or customer presentation.