Forward Deployed Engineer (Healthcare/ BFSI/ Industries)

About us:

Intuitive.AI is one of the fastest-growing (INC 5000, CRN) Cloud & SDx solution and services companies supporting enterprise customers on a global scale. Intuitive is an "Engineering Company" delivering measurable value and key business outcomes.

Intuitive Superpowers:

- DataOps & AI/ML

- Cloud Native, AppSecOps, DevSecOps

- Cloud Migration & Transformation

- Cloud FinOps

- Cybersecurity (App/Data/Infra) & GRC

- SDx & Digital Workspace

We are proud to partner with some of the world's leading enterprises and serve 200+ customers across different industry verticals. We have achieved many milestones along the way, including being recognized as a top-10 fast-growth 150 IT company in the Americas by CRN in 2022 and being named one of America's fastest-growing private companies by INC 5000 in 2022. That’s not all! Even CIO Review awarded us as the Most Promising Cloud Migration Company and Artificial Intelligence Solutions Provider in 2022.

About the job:

Title: Forward Deployed Engineer, [Healthcare, BFSI & Industries]

Location: Remote across USA/ Canada.

Travel: Yes (50%)

Position Type: Full Time

About the role

Forward Deployed Engineers work with our enterprise customers, inside their environments, turning ambiguous business problems into working systems. The role starts at the first technical conversation and does not end until the system runs: scope the work, build the first version, own the outcome throughout.

This is not a demo role and it is not a pure delivery role. Fully specified tickets are rare here. The job is to walk into an organisation that cannot answer an important question about its own operations, and leave with that question answered.

What the role involves

• Sit with customer teams to understand the business problem before proposing a technical one.
• Get into real systems fast: connect to sources, profile the data, and establish what is actually true rather than what the documentation claims.
• Design and build the first working increment on a modern lakehouse and AI stack, in the customer’s environment.
• Scope honestly. Write the technical case a statement of work is built on, and stand behind the estimate.
• Work with the customer’s security, network, and compliance teams to get the environment the build needs.
• Hand over cleanly to the delivery team and move on to the next problem.

What we are looking for

Experience and shape

• 10-15 years building data and software systems, with meaningful time spent in front of customers.
• Deep expertise in one of: the domain, modern data and AI engineering, or enterprise cloud infrastructure. Working knowledge of a second. Genuine curiosity about the third.
• A track record of personally shipping, not only directing. Something built in the last twelve months.

Data and platform engineering

• A modern lakehouse or cloud warehouse in production. Databricks preferred; Snowflake, BigQuery, or Synapse experience transfers directly. Delta or Iceberg table formats, medallion layering, partitioning and file sizing, and control of compute cost.
• Strong SQL, including query tuning and reading an execution plan. Python for engineering work; Scala or Java is useful.
• Spark at production scale: joins, skew, shuffle, and incremental processing.
• Batch and streaming: change data capture out of operational systems, late-arriving data, idempotent reprocessing, and reconciliation back to the source.
• Pipeline discipline: orchestration (Workflows, Airflow, or equivalent), dbt or similar transformation tooling, tests, version control, and CI/CD applied to data.
• Migration off a legacy estate is a strong plus: Oracle, Teradata, Hadoop, Informatica, SSIS, or an on-premise warehouse.

Governance and semantics

• Catalog, lineage, and access control in a regulated setting. Unity Catalog or an equivalent, role and attribute based access, row and column level controls, masking and de-identification.
• The ability to build the layer where business meaning lives: a business glossary, agreed metric definitions, a semantic model, or a knowledge graph. This is what separates a report from an answer a regulator will accept.

AI engineering

• Retrieval-augmented and agentic applications built on enterprise data: chunking and embedding, vector search, tool use, and orchestration.
• Making answers verifiable rather than plausible: grounding to governed sources, citations back to the record, guardrails, and an evaluation harness with a real test set.
• Natural-language-to-query systems and an honest understanding of how they fail.
• Serving and operating models: deployment, prompt and version management, latency and cost control. MLflow or an equivalent.

Cloud and infrastructure

• One major cloud at depth (AWS, Azure, or GCP): compute, storage, networking, and identity.
• Getting connected inside a locked-down enterprise: virtual networks, subnets, DNS, firewalls, private connectivity such as PrivateLink or Private Endpoint, and the customer’s change process for approving any of it.
• Identity done properly: SSO federation with SAML or OIDC, service principals, secrets management, least privilege.
• Terraform or equivalent infrastructure as code, Docker, and enough Kubernetes to deploy and debug.
• Observability as a habit: logging, metrics, alerting, and a workable answer to “how would we know this broke”.

Security and compliance

• Data classification, encryption, residency, and audit evidence treated as part of the build, not paperwork added afterwards.
• Able to carry a security review with the customer’s control function and produce the evidence they ask for.

Ways of working

• Scoping under ambiguity. Given a vague problem and no documentation, produces a defensible plan within a day and states clearly what is still unknown.
• Judgement about what to build in six weeks and what to leave out, and the ability to explain the difference.
• Composure in front of a customer, including being told they are wrong in public.
• Clear written communication. This role produces documents that executives read.
• Handover discipline. Documents and transfers rather than becoming the single point of failure.

Nice to have

• Certifications: Databricks Data Engineer Professional or Solutions Architect, a cloud architect certification, or a controlled vertical certification such as Epic.
• Prior consulting, pre-sales, or field engineering experience.
• Public work: open source, writing, or conference talks in the domain.