Experience: 8.00 + years
Salary: Confidential (based on experience)
Expected Notice Period: 15 Days
Shift: (GMT+05:30) Asia/Kolkata (IST)
Opportunity Type: Hybrid ()
Placement Type: Full Time Contract for 12 Months(40 hrs a week/160 hrs a month)
(*Note: This is a requirement for one of Uplers client - US top Auto Inspection company)
What do you need for this opportunity
Must have skills required:
Ai prototyping, Consulting, Systems Integration, AI orchestration, Hardware ecosystem, Prototype Delivery, Solution Architecture, Stakeholder Communication, Cloud Server (Google / AWS), System Design
US top Auto Inspection company is Looking for:
About The Role
The Solutions Engineer is the connective tissue between business needs, physical environments, and the AI-enabled development teams that deliver production software. You design end-to-end solutions, build working prototypes, and produce the solution blueprints that development teams carry from 80% to 100%, including the full customer launch.
You sit with domain experts and stakeholders to extract what they need. You design the full solution: hardware and devices, connectivity, software architecture, and cloud infrastructure. You orchestrate AI coding agents to prove the design works. And then you hand a production-ready blueprint, with running prototypes, to an AI-enabled development team that takes it the rest of the way to launch.
This is not a traditional solutions architect role (designs but does not build) and not a pure engineering role (builds but doesn''''t engage stakeholders). It is both fused into one, with AI as the accelerant.
Core Operating Model
You own the full arc from business problem to handed-off prototype. Development teams own the final mile to production launch. Activity % of Time
What It Looks Like
Requirements gathering & SME translation 20%
Listening to domain experts, organizing feedback, distilling into structured system specs
Hardware & environment assessment 15%
Physical or virtual assessment of work environments device selection, connectivity architecture System design & architecture 20%
Database schemas, API contracts, module boundaries, CLAUDE.md / AGENTS.md files
AI agent orchestration & prototyping 25%
Session PRDs, parallel agent dispatch, quality gates, working prototype delivery
Scope negotiation & build-vs-buy judgment 10%
What AI builds, what needs specialists, what gets cut, negotiated with stakeholders
Handoff & development team enablement 10%
Solution blueprint packaging, prototype documentation, Q&A with dev team at handoff
What You''''ll Do
- Hardware & Environment Assessment
Visit or virtually assess physical work environments (inspection stations, clinics, warehouses, field sites) to understand operational context.
Catalog existing hardware identify gaps, redundancies, and integration opportunities.
Recommend specific devices (tablets, cameras, scanners, gas analyzers, OBD readers, networking equipment) based on use case requirements.
Design connectivity architectures: Wi-Fi, cellular, offline-first with sync, local caching, accounting for real-world constraints such as dead zones, harsh environments, and shared networks.
- Requirements Gathering & SME Translation
Interview subject matter experts to extract business requirements, listening to what they actually need versus what they initially ask for.
Observe workflows in physical environments to surface requirements that SMEs consider habitual and forget to mention.
Organize raw feedback from domain experts, executives, and end users into structured product specifications that development teams and AI agents can execute against
Translate domain-specific language: inspection protocols, emissions standards, compliance frameworks, diagnostic codes, into system requirements with clear acceptance criteria.
- End-to-End Solution Design
Produce solution blueprints spanning all five layers: physical devices → device interfaces → connectivity → software requirements → cloud infrastructure.
Specify how each piece of hardware interfaces with the software layer: which API, which SDK, what data format, what protocol.
Design database schemas, API route structures, authentication flows, and multi-tenancy models.
Document architectures in machine-readable formats (CLAUDE.md, AGENTS.md) that AI agents and development teams can follow without deviation.
Define the boundary between AI-buildable components and those requiring specialist engineers: proprietary device integrations, firmware, security-critical flows, complex algorithms.
- AI Agent Orchestration & Prototype Delivery
Write session-level PRDs with scoped requirements, explicit acceptance criteria, in/out boundaries, and test count targets.
Dispatch 3–4 parallel AI coding agents (Claude Code, Cursor, or equivalent) across independent modules, verify outputs, and merge results into a coherent codebase.
Enforce quality gates: working builds, meaningful test coverage, and validated deployments before handing off.
Maintain persistent knowledge systems: structured memory files, architecture documents, learnings databases, which carry across sessions and prevent repeated mistakes.
Deliver a running prototype to the development team, not just a spec document.
- Stakeholder Negotiation & Scope Management
Interface with stakeholders: product owners, executives, compliance teams, operations managers, to negotiate practical scope.
Make tradeoff recommendations with supporting data: effort estimates, hardware costs, integration complexity, timeline impact.
Push back on scope creep constructively champion ''''good enough to ship'''' over ''''perfect but never finished'''' while maintaining genuine quality standards.
Represent the development team''''s constraints to stakeholders represent stakeholder priorities back to the team.
- Development Team Handoff & Enablement
Package solution blueprints in formats that AI-enabled development teams can execute against: hardware layout, connectivity architecture, software spec, build-vs-specialist map, explicit scope boundaries.
Conduct structured handoff sessions with development teams: walk through the prototype, clarify the spec, pre-answer the predictable questions.
Remain available post-handoff for scope questions, requirement clarifications, and integration boundary decisions.
Review development team output against the original solution blueprint at key milestones.
Required Skills
Skill
What It Means in Practice
Physical-digital systems thinking
You can walk into an inspection station and sketch the data flow from a gas analyzer through a tablet to the cloud. You understand how hardware devices connect to software systems (Bluetooth, USB, serial, Wi-Fi, APIs, SDKs) even if you don''''t write the integration code.
Requirements engineering
You can sit with a compliance officer, an operations manager, or an inspector and produce a structured spec that captures what they need. You listen for the real problem underneath the stated request. You have done this before.
AI agent fluency
You have shipped real products, not demos, using Claude Code, Cursor, Copilot, or equivalent. You know how to constrain agents, prevent scope creep, verify outputs, and recover when they deviate.
AI boundary judgment
You understand what AI coding agents handle reliably (CRUD, UI components, test generation, standard patterns) and where they fail (novel algorithms, security-critical code, complex state machines, performance-sensitive paths). You know when to hand work to a specialist.
Solution architecture
You can design an end-to-end system spanning physical devices, networking, software, and cloud infrastructure. You understand database normalization, API contract design, and module boundaries.
Stakeholder communication
You can explain technical tradeoffs to non-technical stakeholders without condescending. You can say no to a VP and explain why in terms they accept. You negotiate scope, not just execute it.
Hardware ecosystem literacy
You don''''t need to be an expert in every device, but you know what questions to ask about them. You can evaluate vendor spec sheets, compare device options, and make practical recommendations.
Full-stack literacy
TypeScript / Next.js / React ecosystem primary. You can read code, evaluate test quality, and catch when an AI agent has broken a contract, even though you don''''t write production code from scratch.
Deployment & infrastructure
Comfortable with Vercel, Postgres SQL, AWS services, Docker, and CI/CD pipelines. You can configure OAuth callbacks, debug environment mismatches, and set up GitHub Actions.
Indicators of a Strong Candidate
Has designed solutions that include both hardware and software components, not just one or the other.
Has gathered requirements from non-technical domain experts in physical work environments, not just office or purely digital contexts.
Has shipped products to production using AI coding tools as the primary implementation mechanism.
Can articulate where AI coding agents fail and give specific examples of when they brought in a human engineer (or should have)
Can describe their agent control system, how they scope, verify, and constrain AI output.
Has produced solution blueprints or specs that engineering teams successfully built from
Can describe a time they recommended cutting scope, and the project was better for it.
Has a portfolio with real test suites, not just demo apps, with meaningful coverage across at least one project.
Has worked within a team that included both architects and implementation engineers, with clear handoff points.
Comfortable managing 3–5 concurrent projects without losing context.
Nice to Have
Background in consulting, systems integration, technical pre-sales, or professional services
Domain experience in vehicle inspection, industrial testing, automotive, logistics, or retail stores
Experience with IoT platforms, device management systems, or offline-first architectures
Familiarity with compliance frameworks: emissions standards, safety regulations, inspection protocols
Experience managing mixed teams of AI agents and specialist engineers on the same project.
Experience with MCP servers, AI agent frameworks, or multi-agent orchestration
Hackathon experience compressing multi-week work into 48-hour sprints with AI.
How This Role Connects To The Organization
The Solutions Engineer operates at the boundary between customer-facing problem definition and AI-enabled software delivery. The diagram below shows the flow of work:
Stage
Who Owns It
SME interviews & stakeholder negotiation
Solutions Engineer
Hardware & environment assessment
Solutions Engineer
End-to-end solution blueprint
Solutions Engineer
AI agent orchestration & working prototype
Solutions Engineer
Structured handoff to development team
Solutions Engineer
Build to production launch (80% → 100%)
AI-enabled development team
Milestone reviews against solution blueprint
Solutions Engineer (advisory)
The handoff artifact (solution blueprint) includes: hardware layout and device specifications, connectivity architecture, software requirements with acceptance criteria, build-vs-specialist map, and explicit scope boundaries. The development team consumes this document and the running prototype to carry the product to full customer launch.
How to apply for this opportunity
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- Step 2: Complete the Screening Form & Upload updated Resume
- Step 3: Increase your chances to get shortlisted & meet the client for the Interview!
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