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As AI Quality Architect, you are the person who makes quality native to the agentic SDLC. You design the systems, standards, and intelligence layers that ensure every stage of an AI-accelerated pipeline - from requirement ingestion to autonomous deployment - is observable, trustworthy, and continuously improving. You don't retrofit testing onto AI workflows; you architect quality into them from the ground up.
How will you make an impact?
Agentic Quality Architecture
Design the end-to-end quality architecture for agentic SDLC pipelines - spanning requirement analysis, code generation, test creation, execution, triage, and release gates
Define how quality agents are orchestrated: which decisions they own autonomously, which require human-in-the-loop checkpoints, and how confidence thresholds govern both
Architect multi-agent quality workflows: requirement validation agents, test generation agents, failure triage agents, and regression analysis agents working in coordinated pipelines
Establish trust and verification models for agent-produced artifacts - test code, assertions, coverage reports, and defect analyses must all be auditable and traceable
Own the architectural patterns for quality feedback loops between agents: how a deployment agent learns from a triage agent's findings, and how that signal improves future generation
AI-Native Test Engineering Platform
Design and own the LLM-powered test generation platform - from natural language requirement ingestion to executable, maintainable test output
Architect the evaluation harness that continuously measures test generation quality: coverage delta, false-positive rates, assertion accuracy, and maintenance burden over time
Build the self-healing test infrastructure layer - agents that detect broken selectors, drifted APIs, or changed behaviors and propose or apply fixes autonomously
Define the prompt engineering standards, context injection patterns, and RAG architectures that ground test generation agents in real codebase context
Architect test artifact governance: versioning, ownership attribution (human vs. agent), rollback capability, and confidence scoring for every generated artifact
Quality Gates in Autonomous Pipelines
Design intelligent, adaptive quality gates that operate at the speed of agentic CI/CD - gates that reason about risk, not just pass/fail thresholds
Build risk-scoring models that dynamically adjust gate strictness based on change scope, code origin (human vs. AI-generated), historical failure patterns, and deployment context
Architect the observability layer for agentic pipelines: what signals indicate a pipeline agent is making poor quality decisions, and how are those signals surfaced in real time
Define the integration patterns between quality gates and orchestration platforms (LangChain, LlamaIndex, custom agent frameworks) used across the engineering org
Establish rollback and circuit-breaker patterns for autonomous deployments triggered by quality signal degradation.

Were looking for a highly motivated AI Developer to help design, build, and deploy intelligent agentic systems across our product ecosystem. In this role, you'll work at the intersection of machine learning, backend systems, and modern frontend technologies to deliver AI-first features that feel magical to users.
This is a hands-on, cross-functional role ideal for engineers who love building full-fledged features-from data pipelines and LLM orchestration to intuitive UI experiences-with a strong product mindset.

We are looking for an experienced Back End Tech lead, to further develop the platform. You will join one of our Fullstack teams and help the teams build a product that customers love and use on a daily basis, a product that is fun to use, fast, reliable and scalable. The product has a broad amount of features and serves more than 1.5M active users, leading to many product and engineering challenges.
A Tech Lead is a hands-on engineer who leads by example, bridges the gap between strategy and execution, and strengthens the effectiveness of their team. This role is distinct from that of a Senior Developer through a clear set of responsibilities that focus not just on individual excellence but on team-level technical leadership, decision-making, and operational maturity.
Key Responsibilities;
Lead complex projects, own technical roadmap and architecture decisions
Actively contribute high-quality code to critical areas, set standards for the team
Drive tech standards, lead testing coverage, monitor KPIs, ensure excellence before shipping
Own platform stability, lead major incidents, drive root cause analysis and action items
Coach team through code reviews, technical sessions, and content; be the go-to technical expert
Lead the teams technical roadmap
Conduct technical interviews, run onboarding, represent team in cross-functional forums

The Chief Architect at our company Actimize owns the technical vision that holds this portfolio together. The mandate is to ensure that a wide-ranging set of products remains coherent, secure, scalable, and economical to build and operate regardless of where and how it is deployed. This is a hands-on leadership role at the intersection of deep technical authority and enterprise strategy: you will set architectural direction, resolve the hardest cross-cutting trade-offs, and raise the engineering bar across the organization.

This is the most senior individual-contributor-plus-leadership technical role in the company. You will be a trusted advisor to the executive team and the final technical authority on architecture decisions that span products.
How will you make an impact?
Architectural Vision & Strategy
Define and own the multi-year target architecture across the entire product suite, balancing product autonomy with shared platform leverage.
Establish an architecture strategy so that products can be delivered on-premise, in private cloud, in public cloud, or as pure cloud SaaS.
Drive the platform and shared-services strategy (identity, data, integration, observability, deployment tooling) that reduces fragmentation across products.
Help architects translate business and product strategy into technical roadmaps; advise the executive team on build-vs-buy, modernization, and major technology bets.
Design abstractions and packaging models that let a single product run reliably across heterogeneous environments with differing scale, network, data residency, and operational constraints.
Establish reference architectures and patterns for tenancy, configuration management, release/upgrade cadence, and feature parity across on-premise and cloud-hosted deployments.
Security, Compliance & Resilience
Embed security, privacy, and regulatory compliance into the architecture by design - addressing the standards relevant to financial services (e.g., data protection, auditability, segregation, encryption, and regional data residency).
Define non-functional requirements and architectural guardrails for availability, disaster recovery, transactional integrity, performance, and observability appropriate to mission-critical financial systems.
Partner with Security, Risk, and Compliance leadership to ensure the architecture withstands audit and regulatory scrutiny across jurisdictions.
Governance & Standards
Establish and run an architecture governance function: standards, decision records, review processes, and a technology radar - proportionate enough to enable speed rather than obstruct it.
Evaluate and select core technologies, platforms, and strategic vendors; own the long-term technology stack direction.
Leadership & Influence
Lead and mentor a community of architects and senior engineers; grow architectural capability across teams.
Build consensus across product, engineering, security, operations, and commercial stakeholders, and make decisive calls when consensus isn't reachable.
Represent the company's technical vision with key customers, partners, and prospects, and in due-diligence and pre-sales conversations for strategic deals.

we are looking for an experienced Data Analyst to work with us to drive insights and create stronger visibility in the organization. You will join an innovative, high-performance team and work with cutting-edge technologies in a dynamic environment.
Key Responsibilities:
The role is focused on the marketing domain, working closely with growth, performance marketing, and user acquisition teams.
You will analyze marketing funnels, campaign performance, and attribution models to optimize ROI and drive scalable growth.
This position requires a strong understanding of marketing metrics (CAC, LTV, conversion rates) and the ability to translate data into actionable marketing insights.
Build Specialization-specific operational dashboards.
Develop an analytics framework in collaboration with business process improvement and operations management.
Quarterly planning and roadmapping in collaboration with operations management and product team.
Write monthly Specialization-specific performance reports; written reports should show methodology with detailed statistical analysis, a summary of key findings, and recommendations.
Incubate, operationalize, and streamline Product launches; including Friction Optimization to improve user experience.

As a Senior Data Science Researcher in our Workforce Management group, you will be part of a cross-sectional team working at the intersection of machine learning, GenAI, agentic AI, ML-Ops and Operations Research. You will develop the next generation of our companys Workforce Management solution for our customers contact centers - forecasting work volume, scheduling agent shifts, managing time-off requests, and owning the full lifecycle of the work schedule, in a high-stakes production environment serving many customers. Our solution has been recognized as the market leader for over 10 years.
How will you make an impact?
Developing end-to-end ML systems, tailored to specific project needs, to train and serve production-level models in a high-stakes environment.
Deploying GenAI and agentic AI systems to customers in production, improving on existing solutions or creating new services from scratch.
Designing and building agentic AI flows - tool use, multi-step reasoning, and orchestration.
Forecasting work volume and optimizing shift schedules using time-series methods and mathematical optimization.
Integrating new features and solutions into large existing software systems, stringing multiple disciplines together.
Running unstructured evaluation processes - finding noteworthy phenomena, experimenting, and feeding conclusions back to the team.
Owning projects end-to-end, leading team members and colleagues to efficient planning and delivery.