• Design, build, and ship agentic workflows across multiple domains (research agents, coding assistants, conversational agents (voice, texts, etc), reasoning agents, scheduling agents, analytics agents, workflow automation bots, etc.) • Own the end-to-end lifecycle: data ingestion → reasoning → action taking → evaluation → monitoring. • Build multi-step agents capable of autonomous planning, context tracking, memory, tool use, and API orchestration. • Architect systems using modern agent stacks (LangChain, LlamaIndex, OpenAI Assistants, Model Context Protocol (MCP), custom orchestration). • Build robust retrieval pipelines (RAG), vector embeddings, caching layers, and knowledge grounding systems. • Deploy agents as microservices with proper observability, evals, guardrails and fallbacks. • Optimize inference cost, latency, accuracy, and task-completion rates. • Run systematic evaluations: function calling accuracy, groundedness, hallucinations, long context stability. • Create reusable frameworks and libraries to accelerate subsequent agent builds. • Comfortable building MVPs in days and scaling them to stable production within weeks/months • Drive technical and architectural decisions to meet product requirements while also anticipating and designing for future needs • Design and develop new software and enhance existing software for clients’ systems, and for Cority’s base software. • Communicate directly with Product Owners to ensure that requirements and specifications are understood. • Develop high-quality software and is an advocate of automation frameworks for testing, integration, and deployment. • Review completed software designs or prototypes with team and participates in code reviews • Mentor junior developers. • Open to learning and working with modern technologies as required in the project.

• Work with Imogen to modernize production mainframe systems in collaboration with customers and partners • Work as part of small, cross-functional XP teams, pairing daily and collaborating closely with product managers, designers, client engineers, partners, and internal platform teams • Lead and facilitate technical discovery sessions and workshops, including on-site with customers • Apply XP engineering practices such as pair programming, TDD, refactoring, and CI/CD • Use and experiment with LLM-assisted coding tools • Design and operate production systems with a focus on durability, operability, and eventual client and partner ownership • Execute delivery work in a way that enables partner engineers to independently deliver modernization efforts • Collaborate closely with partner engineers through pairing, mentoring, and shared problem solving • Work flexibly across time zones when collaborating with partners

• The Full-stack Software Engineer will build the work needed to implement functionality from the product backlog. • Tests deliverables against a user story’s acceptance tests and unit tests; • The Full-stack Software Engineer will execute necessary documentation as directed or needed. • Works closely with other team members to ensure that features meet business. • Analyze and decompose complex user stories and epics into manageable tasks. • Collaborate with project managers and team members to plan and estimate sprint tasks. • Offer expertise in architectural and design decisions, as a subject matter expert in SQL, C#, .NET, REST, JSON, and Angular(or React) technologies. • Guide and mentor junior developers, fostering their growth and understanding of industry best practices. • Craft high-quality, scalable, and secure code. • Conduct thorough code reviews (PRs) to uphold code quality and adherence to design principles. • Identify, troubleshoot, and enhance existing software solutions. • Collaborate closely with engineers, product managers, and stakeholders to maintain clear communication and streamline workflows. • Communicate technical concepts effectively to non-technical team members. • Facilitate team code reviews, training, and planning sessions. • Operate effectively in high-stress environments, managing multiple priorities concurrently. • Quickly adapt to changes in requirements or technologies. • Maintain composure and clarity of thought under pressure.

• Evolve a modern, AI-first quality strategy for our fast-scaling SaaS architecture, including foundational infrastructure and emerging agentic/intelligent systems. • Integrate AI enhancements into CI/CD pipelines (e.g., predictive flakiness detection, automated test generation, self-healing scripts) to improve isolation, data setup, & execution reliability using existing/suggesting tools. • Establish scalable testing practices that support hyper-growth and petabyte-scale AI data pipelines. • Design deterministic and statistical testing approaches for non-deterministic LLM-based and agentic systems, addressing hallucinations, prompt injection, bias, drift, and safety risks. • Build automated evaluation pipelines and harnesses for correctness, faithfulness, retrieval quality, generation accuracy, tool-calling, planning sequences, and multi-agent flows. • Execute/Develop test frameworks for the full AI lifecycle: prompts, datasets, embeddings, model versions, RAG pipelines (end-to-end validation), and guardrails. • Implement red-teaming, bias/fairness checks, and compliance mechanisms; leverage in-trend frameworks for metrics and observability. • Partner closely with product, data science, AI engineering, and dev teams to test AI features, conduct multi-agent simulations, and ensure high-quality roadmap delivery. • Facilitate knowledge sharing and upskilling on AI testing best practices across the Quality Function. • Drive core metrics (DORA, test coverage/effectiveness) plus AI-specific indicators (e.g., hallucination rate, context precision, drift detection). • Build real-time dashboards and support A/B testing of models with post-deployment monitoring. • Champion a quality-first, ethical AI mindset organization-wide. • Mentor SDET’s, lead workshops on AI risks/validation, and influence design/deploy/incident processes. • Define roadmaps and best practices for sustainable AI quality assurance.