Alliance Analytical
Website:
aaisolutions.com
Job details:
AAI Solutions builds AI-native platforms, agentic workflows, and modern software systems for organizations that need scalable, secure, and practical AI adoption. We help teams move from fragmented tools and manual processes to intelligent, governed platforms that support real business operations across industries.
We are looking for a Senior Solutions Engineer who thinks in systems, leads through ownership, and knows how to turn ambiguity into working software. This is a hands-on technical leadership role, not a pre-sales position or a purely theoretical architecture role. Depending on the solution area, you may own a product, platform capability, or major feature set from requirements and architecture through delivery.
At AAI Solutions, we practice AI-native development. That means defining intent clearly, creating implementation-ready specifications, orchestrating AI-assisted development responsibly, and ensuring what ships is correct, secure, maintainable, and aligned with product and business goals. This role is central to both the systems we build and the way we build them.
What You'll Do
- Own technical design and architecture decisions for your solution area.
- Translate product requirements into clear technical plans, ADRs, and implementation-ready specifications.
- Drive delivery across roadmap milestones with strong standards for quality, maintainability, security, and compliance.
- Break down work into clear, executable tasks for engineers and AI agents.
- Orchestrate multi-agent workflows where implementation, testing, and review are handled through defined stages and quality gates.
- Implement proof-of-completion practices so work is not considered done until tests pass, outputs are verified, and deliverables are checked against the original spec.
- Review implementation output for correctness, architecture alignment, security risk, redundant abstraction, and drift from requirements.
- Partner closely with product and business stakeholders to refine requirements, challenge assumptions, and surface technical risks early.
- Help strengthen shared engineering standards, governance practices, and AI-native delivery workflows across the team.
- Mentor engineers on structured, specification-first development and disciplined use of AI coding agents.
What We're Looking For
Required
- 7+ years of software engineering or architecture experience.
- Demonstrated ownership of complex software solutions from design through delivery.
- Strong proficiency in at least one relevant area of our stack, such as TypeScript/Node.js, React/Next.js, or Python.
- Experience translating product requirements into architecture and implementation plans.
- Hands-on use of AI coding tools such as Claude Code, Cursor, or equivalent.
- Ability to create structured specifications, acceptance criteria, and technical guidance that reduce ambiguity.
- Experience with context engineering across a codebase, including maintaining AI guidance artifacts that shape output quality.
- Experience with cloud platforms such as AWS, GCP, or Azure, and modern API or service-based architecture patterns.
- Comfort operating in a small, fast-moving environment with broad ownership and evolving priorities.
Preferred
- Experience designing and implementing multi-agent workflows in production.
- Familiarity with MCP, A2A patterns, or similar approaches to tool and agent orchestration.
- Experience building evals or quality frameworks for AI-generated output.
- Working knowledge of embeddings, vector search, classification, fine-tuning, and related applied AI concepts.
- Familiarity with managed AI platforms such as AWS Bedrock, Azure OpenAI, or Google Vertex AI.
- Experience with agentic orchestration frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or Google ADK.
- Experience in enterprise software, AI platforms, EdTech, InsureTech, healthcare, financial services, government technology, or other regulated or trust-sensitive environments.
- Familiarity with multi-tenant SaaS architecture, RBAC, row-level security, and AI-related security concerns such as prompt injection or dependency risk.
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