Responsibilities Design, build, customize, and maintain Windows OS images for enterprise environments. Perform OS hardening and security configurations in line with industry standards and organizational policies. Implement and manage Windows OS deployment using Microsoft deployment tools. Develop and maintain automation scripts to streamline OS configuration, deployment, and validation. Ensure OS images comply with security guidelines such as STIG, Nessus, and firewall policies. Troubleshoot OS-level issues related to boot, deployment, drivers, and security. Prepare and maintain detailed technical documentation in compliance with QMS. Collaborate with stakeholders to understand requirements, track progress, and ensure timely delivery. Provide regular status updates and support release execution activities.ExperienceHands-on experience (1–2 years) in OS hardening, configuration, and customization Experience with Windows XP, Windows 7, and integration with third-party tools

Responsibilities Design, build, and test incremental, production-ready solutions that deliver measurable business outcomes. Contribute across the stack (front-end and back-end), with attention to scalability, performance, maintainability, and security-first design. Implement AI-DLC Patterns (e.g., code assistants, chat/agent tools, test generators) to accelerate analysis, coding, refactoring, test creation, and documentation—while maintaining human oversight and accountability. Responsible AI: Evaluate AI suggestions for correctness, security, performance, and style; ensure output meets coding standards and organizational guardrails. Automate the routine: leverage AI to scaffold boilerplate, generate typed API clients, create repeatable test fixtures, and CICD pipelines. Mentor & pair: model effective AI usage in pairing sessions; coach junior engineers on prompt strategies to promote AIAE practices. Strong object-oriented skills in C#; deep experience with API design (REST and/or GraphQL) and Angular (or similar). Cloud-native development (AWS preferred); familiarity with AWS certifications is a plus. Quality focus with automated testing (unit/integration/acceptance). Collaborate directly with business leaders and SMEs to understand ACA’s domain and ensure successful delivery.ExperienceBachelor’s degree in computer science or related discipline 8-12+ years of Software Engineering experience Strong communication skills

ResponsibilitiesDevelop and perform regression testing of existing software Lead the design, creation, and execution of test plans and procedures for complex projects Coordinate with end users to plan user acceptance testing (alpha and beta) Ensure that system tests are successfully completed, documented, and all problems are resolved Establish and communicate common goals and direction for the ITO team Act as a source of direction, training, and guidance for less experienced Engineers Undertake complex projects requiring additional specialized technical knowledge Identify, recommend, and implement changes to enhance the effectiveness of ITO strategies Assist in planning and scheduling testing Work with team leadership to design and structure a testing strategy for all solutions on the platform Reproduce customer issues when needed to support customer-facing teams and the development team ExperienceMinimum 5 Years Techinical Skills - Mandatory SDLC knowledge with Automation testing exposure Testing tools experience (QA/testing) SQL querying for test result analysis API testing (Postman) Query Analyzer, DB scripting, ODBC configuration Cross-functional collaboration (BAs, Development, TPOs) to define test artifacts

ResponsibilitiesSAP ECC Technical (ABAP) support and enhancements. The system has a roadmap to migrate to S4 and this role will become an SAP S4 technical role in the future. Analyzes system operations through monitoring, capacity analysis and root cause analysis to identify and drive change that ensures continuous improvement in system stability and performance. Assists in the overall maintenance and support of the efficient software solutions. Collaborates with technology teams across the Organization to leverage resources and expertise, establish software best practices and standards, and build for partnerships with IT teams. Responsible for program designs, development, unit testing, peer reviews in an SAP ABAP environment. Responsible for identifying development tasks in support of project planning and estimation. Participates in and makes performance recommendations to code during peer reviews. Works with functional and business users to troubleshoot issues in production. Strong knowledge and hands on experience of software development life cycle (SDLC), modeling of business processes, application design patterns, and business/functional documents Supports the building and maintenance of an Enterprise portfolio to support Application capabilities lifecycle and roadmap initiatives. Advises business areas in the analysis of innovative technologies and how they impact the broader Enterprise Architecture Analyzes industry standards and trends in integration technologies. Provides technical leadership, coaching, and mentoring to team members. Conducts SAP unit testing to ensure systems meets user specifications. Requires increased skill in multiple technical environments and knowledge of a specific business area. May participate in the project planning process with team members. May meet with process team to analyze business processes, understand requirements, and recommend technical solutions... May analyzes existing SAP platform to identify weaknesses and develop opportunities for improvements, when assigned. Troubleshoots existing information systems to identify errors or deficiencies and develop solutions.Qualification & ExperienceBachelor’s Degree in Computer Science, Information Technology or any other related discipline or equivalent related experience. SAP ECC experience in a productive environment is required. Current SAP certification is desired. Requires training in fields such as information systems computer sciences or similar vocations obtained through completion of a four-year bachelor’s degree program; requires a minimum of five (7+) years directly related and progressively responsible experience designing building testing and deploying enterprise SAP solutions (certification preferred) . In-depth experience in ABAP Workbench, ABAP Objects, BAPI, BADI’s, IDOCs, BDC and ALV Programming.

ResponsibilitiesBuild and optimize LLM-powered systems for reasoning, retrieval, and adaptive execution Design multi-turn interaction models for contextual planning and collaboration Develop and refine embedding strategies for semantic retrieval and workflow grounding Work with Platform and Product teams to translate research into production systems Contribute to responsible scaling frameworks ensuring explainable, reliable, and secure AIQualification & Experience    5–6 years in applied AI, NLP, or multi-agent systems Experience with LLM optimization, prompt design, and retrieval-augmented workflows Strong Python and familiarity with Hugging Face / LangChain / OpenAI APIs Understanding of evaluation, context management, and memory architectures A research-minded, production-aware approach focused on real-world reliability

ResponsibilitiesDevelop and maintain applications using MongoDB, Express.js, React, and Node.js Build reusable components and scalable REST APIs Optimize performance, security, and application reliability Participate in code reviews, technical planning, and architecture decisions Qualification & Experience    5+ years full-stack JavaScript experience Strong proficiency in React + Node.js Understanding of microservices, authentication, and state management Familiarity with CI/CD, Git, and cloud deployment workflows

ResponsibilitiesImprove Brained’s visual compiler and runtime for performance and reliability Build scalable infrastructure for multi-tenant environments and containerized execution Work with AI, Product, and Security teams to ensure a secure, resilient platform Enhance observability, governance policies, and internal developer tooling Refine deployment pipelines and runtime stability at scale Qualification & Experience    5+ years in platform/backend engineering, ideally in distributed systems Strong in Golang / Python / Rust and container platforms (Docker, Kubernetes) Experience with multi-tenant SaaS, cloud environments (AWS/GCP/Azure) Familiarity with event-driven systems, API design, and observability stacks Product-minded approach to elegant, reliable systems

ResponsibilitiesSit with product, design, and engineering to translate evolving requirements and ambiguous ideas into shipping interfaces. Co-shape interaction patterns for AI-driven experiences—streaming responses, evidence citations, confidence cues, guided workflows—as UX is stress-tested with real users. Build prototypes fast to react to. Iterate from rough designs and feedback rather than waiting for finished specs. Distinguish AI-surfaced output from authoritative or human-approved content visually and structurally—because in enterprise contexts that distinction matters. Build (fast) with AIBuild production-grade front-ends in React 18+ with TypeScript, using Next.js (App Router) or equivalent meta-frameworks (Remix, TanStack Start). Implement streaming UI patterns for chat interfaces and agent interactions—Server-Sent Events, streaming fetch, optimistic updates, partial rendering. Build dense, real-time dashboards with multi-level drill-down (portfolio ? segment ? entity ? task) and multi-view visualizations. Implement data visualizations using Recharts, Visx, D3.js, Plotly, or ECharts—including radar/spider charts, time-series, and custom chart compositions. Build component architectures grounded in design systems and tokens, using Tailwind CSS with shadcn/ui, Radix UI, Material UI, or Mantine. Implement client and server state cleanly: TanStack Query for server state; Zustand, Redux Toolkit, or Jotai for client state. Use AI-assisted development tools (Claude Code, Cursor, GitHub Copilot) through structured workflows, templates, and sub-agents—with discipline and review.Qualification & Experience    3+ years modern front-end engineering experience, production-grade. Strong React 18+ and TypeScript fundamentals, with hands-on Next.js (App Router) or equivalent meta-framework experience. Data visualization experience: radar/spider charts, multi-level drill-down dashboards, real-time data views. Hands-on with at least one of Recharts, Visx, D3.js, Plotly, or ECharts. Streaming UI experience: Server-Sent Events, streaming fetch, real-time data patterns, chat-style interfaces with progressive rendering. Component architecture and design-system implementation. Hands-on with Tailwind CSS and at least one component library (shadcn/ui, Radix UI, Material UI, or Mantine). Strong state management with TanStack Query plus one of Zustand, Redux Toolkit, or Jotai. Forms and validation: React Hook Form + Zod or equivalent. Testing discipline: Vitest or Jest, React Testing Library, Playwright (or equivalent) for E2E. WCAG 2.1 AA conformance, keyboard navigation, ARIA patterns. Active, structured use of AI-assisted development tools (Claude Code, Cursor, GitHub Copilot) with demonstrable workflows. Comfortable iterating from evolving designs and co-shaping interaction patterns rather than executing fixed specs. Strong attention to information hierarchy—you can hold multiple audiences with different mental models in your head on the same product. A real, recent trail of built things: GitHub, a portfolio, side projects, indie tools, or OSS contributions. A no-compromise attitude on clean code, performance, accessibility, and user experience. Bachelor's or Master's in Computer Science, Design Engineering, or a related discipline (or equivalent demonstrable experience).

ResponsibilitiesSit with a business or clinical leader and reframe an idea or problem into something concrete and buildable. Know what to build by the end of the conversation; have a working prototype to react to by the end of the week. Partner closely with product, design, and client stakeholders to translate ambiguous ideas into software that ships. Demo live without a slide deck. Reframe problems out loud. Don't get stuck waiting for someone else to make the decision. Lead POCs, innovation sprints, and internal research experiments to validate emerging AI techniques. Build (fast) with AI When the brief is clear, head down and produce. Build modular backends in Python or TypeScript aligned with clean architecture, OOP, SOLID, and domain-driven design. Create full stack applications, APIs, agents, workflows, and similar systems using frameworks such as Next.js, React, Fast API, Fastify, FastMCP, and Hono. Architect and ship production-grade agentic applications using Lang Graph, AutoGen, Claude Agent SDK, OpenAI Assistants, or your own orchestration layer. Integrate frontier and self-hosted LLMs (Claude, GPT, Gemini, open-weight models) with tools, data, and external systems through MCP and custom connectors. Apply RAG techniques where they actually help: vector databases (Pinecone, Chroma, Weaviate, pgvector), hybrid retrieval with Elasticsearch or Solr, and BM25 + similarity search. Work across relational, document, key-value, and graph stores as the problem demands; use event-driven patterns where they fit, not by default. Design prompt and context engineering frameworks that optimize accuracy, repeatability, cost, and latency. Use AI-assisted development tools (Claude Code, GitHub Copilot, Cursor, Codex) through structured workflows, native instructions, templates, and sub-agents—with discipline and review. Fine-tune or adapt models where the problem genuinely calls for it. Test, Deploy, Productionize Spin up the infra, write the evals, wire up the MCP servers, deploy the agents, and harden the bits that survive contact with real users. Deploy on AWS, Azure, Cloudflare, or Vercel using containerization (Docker, Kubernetes) or serverless—chosen for fit, not preference. Treat evals as a first-class discipline: hands-on harnesses, not theoretical frameworks. Build with a clear-eyed view of where current AI tooling helps and where it falls short. Apply engineering practices that hold up in production: TDD, secrets management and rotation, SAST/DAST, structured logging, metrics, tracing, and automated CI/CD (GitHub Actions, Jenkins). Own what you build end-to-end, including the infrastructure and operations that keep it running. Mentor others on system design, agentic patterns, and AI engineering best practices. Qualification & Experience    3+ years relevant experience building production applications using AI / agentic development approaches—full stack applications, agents, workflows, MCPs, and more. Hands-on experience with agents, not just prompted models. You have wired tools to a model and let it run multi-step using Lang Graph, AutoGen, Claude Agent SDK, OpenAI Assistants, or your own orchestration. Active, structured use of AI-assisted development tools (Claude Code, Cursor, GitHub Copilot) with demonstrable workflows, sub-agents, skills, and innovative approaches. Strong Python or TypeScript, with OOP, SOLID, 12-factor application development, and microservice architecture. You've built Next.js applications, Fast API services, and similar. End-to-end implementation experience with vector databases, retrieval pipelines, and eval harnesses. Cloud-native deployment experience across at least one of AWS, Azure, Cloudflare, or Vercel—with Docker, Kubernetes, and GitHub Actions. A no-compromise attitude on clean code, TDD, security, observability, scalability, performance, and cost. A deep working understanding of how LLMs behave—and where they break—and how to optimize accuracy, latency, and cost. Clear writing and a willingness to reframe problems in conversation rather than wait for someone else to define them. A real, recent trail of built things: GitHub, a portfolio, side projects, indie tools, or OSS contributions. A founder's mindset and genuine appetite for ambiguous, high-impact technical challenges. Bachelor's or Master's in Computer Science, Machine Learning, or a related technical discipline.

ResponsibilitiesBuild test plans for AI products across large combinatorial matrices: input variants, user roles, content states, deployment surfaces. Maintain golden-question test suites that catch regressions when models, prompts, retrieval, or content change. Design coverage for the things AI products specifically get wrong: hallucinations, citation drift, prompt injection, context bleed, guardrail bypass, role escalation, stale or unapproved content surfacing where it shouldn't. Translate ambiguous quality requirements into concrete, repeatable test cases—especially where audit trails, access controls, and content provenance matter. Executive & Design Run structured test passes across web UIs and APIs; investigate failures deeply enough to give engineers a useful starting point, not just a screenshot. Validate evidence citations point to the right sources and that "pending review" or restricted content stays excluded from AI responses where the rules require it. Verify role-based access at the UI and API layers—users see what they should and nothing more. Verify data integrity using read-only SQL queries; cross-check what the UI shows against what the database holds. Exercise APIs directly via Postman, Insomnia, or curl; read OpenAPI specs and form requests without help. File defects that are clear, reproducible, and ranked—structured enough that an engineer can act without a meeting. Sustain CoverageMaintain regression coverage as products evolve: prompts change, retrieval indexes update, models get swapped, content gets added. Partner with engineers on evaluation rubrics, golden datasets, and acceptance criteria for AI behaviour—then verify the criteria actually hold in production. Stay current on AI/LLM evaluation tooling and apply what's useful: golden datasets, regression rubrics, hallucination scoring, structured eval frameworks. Maintain test documentation that survives team turnover: clear, structured, and findable. .Qualification & Experience    5+ years manual and automation QA experience in product environments, production-grade. Hands-on experience testing AI / LLM-based products: output validation, hallucination detection, edge-case coverage, prompt injection awareness. Strong test-planning skills for large combinatorial test matrices. Hands-on with test management tooling: TestRail, Zephyr Scale, Xray, or equivalent. API testing fluency: Postman or Insomnia; comfortable reading OpenAPI/Swagger specs and exercising endpoints directly. Browser-based debugging skills: Chrome DevTools (Network, Console, Application), cross-browser verification. Bug tracking and workflow tooling: Jira, Linear, or equivalent. SQL basics: read-only queries to verify data state in PostgreSQL or equivalent. Documentation discipline: clear, structured test plans and defect reports in Confluence, Notion, or equivalent. Comfort with enterprise content workflows, audit-trail verification, and role-based access checks. A clear-eyed view of where current AI tooling helps and where it falls short. Curiosity, persistence, and a willingness to dig past the surface symptom into the underlying cause. Bonus Skills / Experience Understanding of MLOps, model serving, scaling and monitoring workflows (e.g., BentoML, MLflow, Vertex AI, AWS Sagemaker) Exposure to evaluation tools (Ragas, Promptfoo, DeepEval, LangSmith)—read-only or interpretive use is fine. Automation experience with Playwright, Cypress, or equivalent—even if the role is primarily manual. Browser Stack or similar cross-browser/device platforms. GDPR-style data handling verification experience. Pharma, healthcare, or other regulated-industry exposure. Familiarity with EU AI Act or similar AI governance and compliance frameworks