Genius Match

About Us 
We’re building an AI-driven hiring platform designed to transform how organizations evaluate talent. Our product delivers real-time candidate insights, identifies skill gaps, and recommends development pathways - while enabling recruiters to efficiently identify top performers through data-driven decision-making. 

About the Role 
We’re looking for a Psychometrician to join us as a part-time, ongoing consultant. This role combines strategic advisory with hands-on work, ensuring our assessment methodologies are scientifically sound, fair, and compliant with professional and legal standards. 
You’ll collaborate closely with product and engineering to validate and improve our AI-driven assessment systems. 

What You’ll Do 
 

• Evaluate and improve candidate assessment frameworks; 
• Design and run validation studies (criterion-related, construct, content validity); 
• Establish and monitor reliability metrics (e.g., internal consistency, test-retest); 
• Ensure AI-driven scoring aligns with psychometric best practices; 
• Audit algorithms for bias, fairness, and adverse impact; 
• Support calibration and continuous improvement of ranking models; 
• Ensure alignment with EEOC, APA, and SIOP standards; 
• Conduct adverse impact analyses and assess subgroup differences;  
• Recommend and implement bias mitigation strategies; 
• Define and track key measurement KPIs (validity, reliability, fairness); 
• Translate technical findings into clear, actionable insights; 
• Prepare documentation for audits, certification, and external validation. 
  
  Success Metrics 
• Assessment validity and reliability clearly established and documented; 
• Fairness and bias benchmarks defined, tracked, and improved over time; 
• Readiness for certification or third-party validation within agreed timelines. 

Core Requirements 
 

• Master’s or PhD in Psychometrics, Industrial-Organizational Psychology, Statistics, or a related field; 
• 5+ years of experience in assessment validation, ideally in hiring or talent assessment; 
• Strong knowledge of Item Response Theory (IRT) and/or Classical Test Theory (CTT); 
• Experience designing and executing validation studies; 
• Hands-on experience with adverse impact and fairness analysis; 
• Strong analytical and problem-solving skills; 
• Ability to work independently in a remote, flexible environment; 
• Clear communication skills, especially with non-technical stakeholders. 

Nice to Have 
 

• Experience with AI/ML-based hiring or assessment systems;  
• Background in HR tech or talent analytics; 
• Experience supporting audits, certification, or compliance processes;  
• Academic or research affiliation. 

Engagement Details 
 

• Part-time, ongoing consulting role; 
• Fully remote with flexible hours; 
• Mix of strategic advisory and hands-on work. 

Our client is building an end-to-end carbon credit platform on AWS that uses geospatial data, APIs, blockchain-based traceability, and AI-driven audit readiness tooling. We’re looking for a senior engineer to help build core backend systems and interoperability layers for an MVP-stage product.

What You’ll Do

• Build backend services in Python
• Design and implement AWS-native architecture and integrations
• Develop API interoperability with third-party data, audit, and ecosystem partners
• Translate analytical and mathematical problems into algorithms and production-ready code
• Support geospatial/data-science driven workflows
• Document architecture, logic, and technical decisions clearly
• Work closely in a startup environment with fast iteration and regular demos

Requirements

• Strong Python backend engineering experience
• AWS Solutions Architect certification
• Deep experience with AWS services and cloud architecture
• Strong mathematical/analytical background
• Ability to turn models/problems into numerical or algorithmic solutions
• Experience with APIs and systems integration
• Strong English communication skills
• Comfortable wearing multiple hats in a startup setting
• Willing to work on a fully AWS stack only

Nice to Have

• Experience with geospatial systems or environmental/climate tech
• Familiarity with data science libraries in Python
• Experience in audit/compliance-heavy workflows
• Exposure to blockchain-enabled traceability systems

Working Style

• Startup-minded, hands-on, pragmatic
• Strong documentation habits
• Comfortable with agile/Scrum-style workflows
• Able to collaborate with at least 4 hours overlap with US Eastern time (up to 8 pm Kyiv time)

Tech interview will include

1. Live Python coding exercise, AWS architecture/integration exercise, Analytical-to-algorithmic problem-solving discussion

Our client is the leader in the Early Childhood Education (ECE) sector, providing the most widely used developmental assessment and curriculum system in the US. 

They are early in shaping how AI changes the way they build and operate software. They have active initiatives in AI tooling cost-management and agentic development workflows, but their direction is still forming and will keep evolving as the field does. 

They are not looking for someone to execute a fixed plan, they are looking for an experienced practitioner who can validate the direction we are setting, bring expertise we do not have in-house, stay ahead of what is emerging, and help us adjust and grow as the landscape shifts. This person is hands-on enough to build and prove things directly, and senior enough that we trust their judgment on where to place bets.

What you will do

• Act as a hands-on technical partner across AI engineering initiatives, currently spanning tooling cost management, model routing and selection, and agentic development workflows.
• Pressure-test the approaches they are considering, tell them where they are wrong or where a better path exists, and back it with evidence rather than opinion.
• Bring outside expertise and patterns from how other organizations are solving these problems, so they are not learning everything the expensive way.
• Track what is emerging across models, tooling, frameworks, and techniques, and translate it into concrete recommendations on what they should adopt, defer, or ignore.
• Build and run proofs of concept to validate ideas quickly, then help them decide what to operationalize.
• Help mature their agentic development workflow so engineering teams can adopt it, including model routing and token efficiency.
• Partner with Infrastructure and Security so that what they build fits their architecture and meets their data privacy obligations.
• Help them build internal capability, so the expertise compounds with their team and does not leave when the engagement ends.

Required background

• 7+ years in software, platform, or ML engineering, with recent, direct experience building and operating LLM-backed systems in production.
• Demonstrated breadth across the current AI engineering stack: models and their tradeoffs, inference cost dynamics, gateways and routing, agentic workflows, and the AI coding tool landscape.
• A track record of forming a point of view on fast-moving technical decisions and being right often enough to be trusted with them.
• Strong hands-on engineering ability. This person builds, benchmarks, and ships, not just advises.
• Comfort operating with ambiguity and early-stage direction; able to bring structure rather than wait for it.

• Cloud and infrastructure fluency (AWS preferred, including Bedrock).

   

Strongly preferred

• Experience helping an organization adopt AI-first engineering practices, not just individual tool use.
• Hands-on model evaluation and routing design, and exposure to AI cost governance.
• Experience in regulated data environments. 
• A habit of staying current through real practice (building, writing, contributing) rather than headlines.

What this role is not

• Not a fixed-scope implementation seat. The work will change as their direction evolves, and they want someone who helps drive that change.
• Not a data science or model training role. The focus is applied AI engineering, integration, and enablement.
• Not a manager. Individual contributor, hands-on, with influence earned through expertise.

Our client is looking for a Chief Agentic Systems Architect to transform a 10+ year legacy codebase into a high‑velocity, agent‑operable architecture. 

This role sits at the intersection of software architecture, AI‑agent orchestration, and engineering governance. You will design system patterns, MCP interfaces, and cognitive context layers that allow LLMs and autonomous agents to safely refactor, test, and ship production code with minimal human intervention.
 

Responsibilities:

• Decompose legacy monoliths into agent‑readable, modular systems with strict boundaries and single responsibility
• Lead incremental modernization using the Strangler Pattern, wrapping legacy logic in modern, contract‑driven interfaces
• Architect and maintain the agent context layer: standardized Skills, Rules, and Commands for AI‑driven engineering workflows
• Build and operate Model Context Protocol (MCP) servers exposing legacy APIs, services, and databases as typed, secure, AI‑consumable tools
• Own contract‑first API design as the primary interface between human intent and autonomous agent execution
• Act as architectural gatekeeper for AI‑generated pull requests, ensuring scalability, security, and long‑term maintainability
• Mandate test‑driven development (TDD) and characterization testing to preserve legacy behavior during refactoring
• Monitor and optimize agentic reasoning loops to balance cost, speed, and architectural integrity

Requirements:

• 15+ years in software architecture, platform engineering, or technical leadership
• Deep hands‑on expertise with TypeScript, .NET, and Node.js
• Proven experience modernizing large, undocumented legacy systems
• Strong background in API design, distributed systems, and modular architectures
• Practical experience with agentic development, MCP, LLM tooling, or AI‑assisted engineering
• Bias toward clean code, deterministic systems, and production‑grade AI 

• Availability to work until 2 pm - 3pm US EST

   

Will be a plus:

• Experience with remote‑first or globally distributed teams
• Background in SaaS transformations, scale‑ups, or PE‑backed environments
• Comfort operating in high‑ambiguity, high‑ownership settings

Why this role matters:

• You are not adding AI features.
• You are designing the architectural substrate that allows AI to safely build software at scale.
• This is a career‑defining role for architects shaping how autonomous systems write, change, and govern production code.

ROLE OVERVIEW 

As our client's Chief Agentic Quality Architect, you will orchestrate the transition from traditional scripted testing to an AI-augmented quality ecosystem.

Your primary goal is to leverage agentic AI tools to generate, execute, and maintain high-fidelity test suites that keep pace with a development environment where AI agents are writing a significant portion of the production code. 

WHAT YOU'LL DO 

1. Assess & Benchmark Quality Coverage 

• Take ownership of a new product or suite of products as a hands-on quality leader. Conduct a comprehensive audit of the existing test estate across all layers — unit, integration, API, UI, sanity, and regression — evaluating depth, coverage, and reliability of each suite. 
• Evaluate the current QA tooling, frameworks, and automation maturity against client's quality benchmarks and coverage expectations. Identify systemic gaps, test debt, and high-risk areas with no automated coverage. 
• Produce a Current State & Gap Coverage Report that maps existing tooling, highlights missing coverage domains, and recommends the adoption tooling needed to close the gaps — the baseline for every roadmap that follows. 

2. Define the Quality Engineering Roadmap 

• Define missing test cases and user journeys. Prioritise end-to-end automation across critical business flows using agentic automation patterns, with a deliberate emphasis on left-heavy coverage — maximising unit and integration depth first, and extending rightward through black-box UI and regression testing. 
• Architect and own the phased quality engineering roadmap, structured as three delivery horizons: 
• Two-Week Plan: Complete the current-state assessment; identify the highest-risk coverage gaps; stand up quick-win automation on the most critical user journeys; evaluate and select the tooling to be adopted. 
• One-Month Plan: Core regression coverage established across primary domains; CI/CD quality gates operational and integrated into the development pipeline; agentic test generation producing its first validated suites. 
• Three-Month Plan: End-to-end agentic automation live and self-maintaining; Client's benchmark coverage achieved; AI guardrails enforced across all automated agent output; quality metrics visible to engineering leadership. 

3. Agentic Test Generation & Automation 

• Prompt-Based Engineering: Utilise AI agents to automatically transform business requirements and manual test cases into executable Playwright or Cypress scripts. 
• Synthetic Test Creation: Implement tools that autonomously generate test data and edge-case scenarios that human testers might overlook. 
• Autonomous Maintenance: Deploy self-healing automation frameworks that use AI to detect UI changes and update test selectors without human intervention. 

4. Guardrails for AI-Generated Code 

• Agent Regression Strategy: Design and own a comprehensive regression suite specifically tuned to catch the non-deterministic “hallucinations” or logic errors common in AI-generated code. 
• Behavioral Locking: Implement characterisation testing patterns to “lock in” the expected behaviour of legacy systems during AI-assisted refactoring. 
• Autonomous Output Validation: Define quality gates to validate the outputs of autonomous agents, ensuring they meet functional, security, and performance boundaries. 

5. Build & Mentor an AI-Skilled QA Team 

• Hire QA engineers with deep, hands-on capability in AI-assisted testing and agentic automation frameworks — not generalists, but specialists who can design, implement, and operate AI-driven quality systems in production. 
• Mentor and upskill the team on agentic workflows, guardrail frameworks, and quality benchmarks. Set the technical bar and hold the team to it through code reviews, pairing, and structured coaching. 
• Instil a quality-first engineering culture where QA is embedded throughout the development lifecycle — not a downstream gate — and every AI-generated change is treated as a quality risk until validated. 

WHAT WE'RE LOOKING FOR 

• 10+ years in QA automation engineering, SDET, or test architecture roles 
• Automation Frameworks: Expert-level proficiency in Playwright, Cypress, or Selenium. 
• AI Tooling: Hands-on experience using LLMs (Claude, GPT-4, etc.) and agentic frameworks to generate code or automate workflows. 
• CI/CD Mastery: Deep understanding of integrating quality gates into AWS-based pipelines or similar environments. 
• Architectural Mindset: Ability to design "Behavioral Snapshots" to safeguard critical business logic during rapid transformations
• Availability to work until 2 pm - 3 pm US EST

NICE TO HAVE 

• Experience in remote-first or globally distributed engineering teams 
• Background in SaaS scale-ups, legacy modernization projects, or PE-backed environments 
• Exposure to performance, load, or security testing tooling 
• Comfort operating in high-ambiguity, high-ownership environments with evolving requirements