Director, AI Solutions Engineering - BFSI

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.

Job Description:

Director, AI Solutions Engineering - BFSI

Position Type: Full Time

Travel: Upto 50% across USA

Location: Remote (Preferred to be around NJ/NY)

Role Overview

The Director, Solutions Engineering sits at the intersection of executive business strategy and technical architecture. This person leads customers from a business problem to a validated, scoped technical solution — engaging C-level and senior stakeholders on outcomes, whiteboarding the technical approach, and partnering with Solution Architects to pressure-test and estimate the detailed implementation.

Core Responsibilities

1. Engage C-level and executive stakeholders to identify desired business outcomes and map them to technical solution directions.
2. Lead discovery calls and pre-sales workshops to scope customer requirements.
3. Define and whiteboard high- to mid-level technical solutions, owning solution design and scoping through to a defined approach.
4. Establish directional sizing, effort, and timeline estimates for proposed technical solutions.
5. Partner with Solution Architects to define, validate, and pressure-test low-level technical solution designs.
6. Partner with Solution Architects to build detailed effort estimates and Statements of Work (SOWs) for solution implementation.
7. Build and deliver client-facing proposals and pre-sales presentations.
8. Own the hand-off from pre-sales to delivery, ensuring continuity of solution intent and context.
9. Drive account growth strategy, proactively identifying cross-sell and up-sell opportunities.
10. Establish and maintain customer relationships through onsite meetings, events, and other engagement.
11. Support Alliance partnerships through partner events (e.g., technical talks) and maintaining partner competencies.
12. Serve as a technical thought leader, representing the company's point of view externally.
13. People manager and functional lead for Solution Architects

REQUIRED SKILLSETS

Cloud Engineering & Architecture

1. Expert-level proficiency in at least one hyperscaler platform (GCP, AWS, or Azure) with working knowledge of the remaining two, including IaaS, PaaS, managed Kubernetes, and cloud-native networking.
2. Hands-on experience with hybrid cloud architecture, workload placement strategy, and cloud migration frameworks (Rehost / Replatform / Refactor / Retire).
3. Practical understanding of cloud-native security controls, IAM, policy-as-code, and FinOps cost optimization practices.
4. Familiarity with VMware exit pathways and alternative platforms including Nutanix AHV, OpenStack, Apache CloudStack, and Azure VMware Solution (AVS/GCVE).

Network Engineering

1. Deep understanding of enterprise network architecture including SD-WAN, SD-LAN, spine-leaf fabric design, and next-generation campus/branch networking.
2. Proficiency with leading network vendors including Cisco (IOS-XE, Meraki, Viptela), Palo Alto Networks, Juniper, Arista, and Fortinet.
3. Working knowledge of AIOps-driven network operations, telemetry, observability, and automated remediation platforms.
4. Familiarity with cloud networking constructs across AWS (TGW, VPC, AWS Network Firewall), Azure (vWAN, AFD, Azure Firewall), and GCP (Cloud Router, VPC, Cloud Armor).

Cybersecurity

1. Practical knowledge of Zero Trust architecture principles, micro segmentation, SASE/SSE, and identity-based access frameworks (NAC, ZTNA).
2. Understanding of security operations, threat detection, SIEM/SOAR platforms, and vulnerability management in enterprise environments.
3. Awareness of regulatory compliance frameworks relevant to BFSI, Healthcare, and Government sectors (PCI-DSS, HIPAA, NIST 800-171, FedRAMP, CMMC).
4. Ability to articulate security posture improvement as a business outcome, not just a technical control set.

Hybrid Cloud & On-Premises Infrastructure

1. Broad familiarity with datacenter infrastructure including server compute (HPE, Dell, Cisco UCS), SAN/NAS storage (NetApp, Pure, Dell PowerStore), and HCI platforms.
2. Experience with infrastructure assessment methodologies, workload dependency mapping, and TCO/ROI financial modeling for stay-vs-migrate decisions.
3. Understanding of AI-ready infrastructure requirements including GPU compute (NVIDIA H100/H200), NVMe-oF storage fabric, and high-bandwidth AI networking (InfiniBand, RoCE v2).
4. Familiarity with disaster recovery design, backup platforms (Veeam, Cohesity, Commvault), and RTO/RPO-aligned architecture patterns.

Platform Engineering & DevOps

1. Working knowledge of Internal Developer Platform (IDP) concepts, Golden Paths, and platform-as-a-product delivery models.
2. Familiarity with CI/CD toolchains, Infrastructure-as-Code and GitOps practices.
3. Understanding of Kubernetes-native platform engineering, container orchestration, and developer experience (DevEx) tooling.
4. Ability to articulate the business case for platform engineering investment in terms of developer velocity, operational reliability, and cost reduction.
5. Understanding of API design - RESTful APIs, versioning, error handling, pagination, and building interfaces that developers and automation consumers depend on.
6. Experience in designing control planes, reconciliation loops, or state machines for resource lifecycle automation.
7. Understanding of distributed systems principles - eventual consistency, failure handling, idempotency, and resilience patterns.
8. Experience designing systems that integrate LLM-based conversational agents with backend automation/tooling, including guardrails, tool invocation constraints, and human-in-the-loop controls.

Data & AI

1. Strong understanding of agentic AI architecture including multi-agent orchestration frameworks, tool-use patterns, LLM integration, and autonomous workflow design.
2. Familiarity with leading AI development ecosystems including Google ADK, LangChain/LangGraph, Anthropic Claude API, and OpenAI.
3. Awareness of AI infrastructure requirements for enterprise deployment: model hosting, RAG pipelines, vector databases, MLOps/LLMOps platforms, and AI governance.
4. Ability to translate agentic AI use cases into concrete infrastructure and platform requirements for client environments.
5. Understanding of AI safety, hallucination mitigation, and enterprise-grade AI governance principles relevant to regulated industries.
6. Data architecture patterns: data warehouse, data lake, lakehouse, data mesh — knows when to recommend each based on client maturity and use case
7. Cloud-native data platforms: Snowflake, Databricks, BigQuery, Redshift, Synapse, plus the underlying GCP/AWS/Azure data services (Dataflow, Glue, ADF, EMR, Dataproc)
8. ETL/ELT tooling and pipeline design: Airflow, dbt, Fivetran, Informatica, or equivalent; batch vs. streaming (Kafka, Pub/Sub, Kinesis)
9. Data modeling: dimensional modeling, medallion architecture, schema design for both transactional and analytical workloads
10. Data governance and quality: cataloging, lineage, master data management, data contracts (tools like Collibra, Alation, or cloud-native equivalents)
11. Data security and compliance: encryption, tokenization, PII handling, and mapping architecture to frameworks the client
12. Knowledge graph / ontology-based approaches for infrastructure or data intelligence use cases
13. Ability to translate a client's AI ambition into a scoped, sequenced architecture (data readiness → pipeline → model/LLM layer → application layer)

Application Modernization

1. Working knowledge of modernization strategy frameworks (the 6 Rs — rehost, replatform, refactor, rearchitect, rebuild, replace) and the ability to match assessment findings to the right strategy per application rather than defaulting to a full rewrite.
2. Experience decomposing monoliths into services using domain-driven design (bounded contexts, aggregates) and the strangler fig pattern to incrementally carve out functionality without a big-bang cutover.
3. Understanding of legacy-to-cloud-native migration patterns — containerizing existing workloads, adapting to 12-factor app principles, and sequencing database, application, and integration layer moves independently to de-risk cutover.
4. Ability to assess technical debt and legacy risk (unsupported runtimes, undocumented dependencies, tightly coupled data access) and translate findings into a prioritized, business-justified modernization roadmap.
5. Experience with database modernization — schema decoupling, data migration strategies (dual-write, CDC-based sync), and moving from monolithic relational stores to purpose-fit polyglot persistence where warranted.
6. Familiarity with event-driven architecture as a modernization lever — introducing message brokers or event streams to decouple legacy synchronous call chains and enable incremental service extraction.
7. Understanding of API-led integration for legacy exposure — wrapping legacy systems behind well-designed APIs so new services and consumers aren't blocked on full backend replacement.
8. Experience designing and executing characterization/regression test harnesses to safely refactor undocumented legacy code without breaking existing behavior.
9. Ability to modernize CI/CD and delivery practices alongside the application itself — legacy apps often ship manually, so pairing the code modernization with pipeline and IaC modernization is part of the scope, not an afterthought.
10. Working knowledge of AI-assisted code modernization and migration tooling for automated code analysis, dependency mapping, and refactoring acceleration at scale.
11. Ability to articulate the business case for modernization investment in terms of reduced operational risk, faster feature velocity, lower total cost of ownership, and improved talent retention (fewer engineers willing to maintain legacy stacks).

Pre-Sales Acumen & Communication

1. Demonstrated track record of leading technical pre-sales cycles for complex, multi-tower professional services or technology engagements.
2. Exceptional written and verbal communication skills, with the ability to tailor technical narrative for both executive and practitioner audiences.
3. Experience producing client-facing deliverables including SOWs, RFP responses, architecture decks, and board-ready business cases.
4. Comfortable owning the room in discovery workshops, technical deep-dives, and competitive bakeoffs.
5. Structured problem-solving mindset with the ability to qualify scope ambiguity, manage stakeholder expectations, and shape winning proposals under time pressure.

PREFERRED EXPERIENCE

1. 10+ years of experience in enterprise IT with at least 5 years in a client-facing pre-sales, solution architecture, or technical advisory capacity.
2. Experience working within a professional services or IT consulting firm serving enterprise or Fortune 1000 accounts.
3. One or more professional cloud certifications (GCP Professional Cloud Architect, AWS Solutions Architect Professional, Azure Solutions Architect Expert).
4. Experience with verticals including BFSI, Healthcare & Life Sciences, Manufacturing, Energy, or Government is strongly preferred.