ResponsibilitiesImplement and improve logging, monitoring and alerting Build and maintain highly available production systems Optimization of the application for maximum speed and scalability Collaborate with other team members and stakeholders Passion to build things fast, break it and rebuild to evolve a truly world class productQualification & ExperienceHands on experience on source control tools like Git/SVN/Bitbucket Experience working on Linux operating system & networking. Hands on experience on CI/CD tools like Jenkins, github actions, Travis CI, GitOps etc. Hands on experience on infra as code tools like Terraform, Cloudformation etc. Hands on experience on any scripting/programming language like bash, python, nodejs, golang etc. Hands on experience on Docker and Kubernetes. Strong understanding of microservices paradigms and experience working on logging, alerting and monitoring frameworks. Working experience on L4 and L7 load balancers like Nginx / Envoy / HAProxy. Administering SQL/NoSQL databases like MYSQL/Cassandra/Mongo/Postgres. Hands-on experience in Azure cloud. Experience architecting 3-tier applications.

ResponsibilitiesAssist in developing and maintaining backend services and APIs Write clean, scalable, and efficient code under the guidance of senior engineers Work with databases for data storage, retrieval, and optimization Support the integration of third-party APIs and services Participate in debugging, testing, and troubleshooting backend applications Contribute to system design discussions and backend architecture improvements Collaborate with frontend developers and cross-functional teams to build product features Help monitor application performance and resolve technical issues Follow coding standards, documentation practices, and version control processes Qualification & ExperienceStrong understanding of programming fundamentals and data structures Proficiency in at least one backend programming language (Python, Java, Node.js etc.) Basic knowledge of SQL and relational databases Understanding of REST APIs and web application architecture Familiarity with Git and version control systems Problem-solving mindset and willingness to learn new technologies Good to Have: Experience with backend frameworks such as Django, Flask, Spring Boot, Express.js, or FastAPI Knowledge of NoSQL databases like MongoDB Understanding of cloud platforms (AWS, GCP, or Azure) Familiarity with Docker, CI/CD pipelines, or microservices architecture Academic or personal projects involving backend development Basic understanding of authentication, security, and API best practices

ResponsibilitiesDefine and own the DevSecOps reference architecture across the client's cloud estate — landing zones, account/subscription vending, identity, secrets, network segmentation, and workload isolation patterns — applied consistently whether on AWS (preferred), Azure, or GCP. Set the multi-year roadmap for shift-left security, supply-chain integrity, runtime protection, and continuous compliance evidence collection across regulated and non-regulated workloads. Act as the senior technical voice in client steering committees, security architecture reviews, and audit readiness sessions; translate regulatory intent into engineering requirements that teams can implement. Mentor and coach Newpage and client engineers; raise the bar on secure coding, threat modeling, and incident response across the account. Engineer Security Into the Cloud Estate Design and operate hardened, multi-account or multi-subscription landing zones — AWS Control Tower / Organizations / SCPs / Identity Center (preferred), Azure Landing Zones / Management Groups / Policy, or GCP Organization Policy / Folders — with guardrails enforced as code. Build paved-road CI/CD pipelines (GitHub Actions, GitLab CI, AWS CodePipeline, Azure DevOps, or Jenkins) with integrated SAST, DAST, SCA, secrets scanning, IaC scanning, container scanning, and SBOM generation. Implement policy-as-code using OPA/Rego, Checkov, and cloud-native equivalents (AWS Config Rules / CloudFormation Guard, Azure Policy, GCP Organization Policy); enforce at pull-request time and in production. Operationalize cloud-native security services end-to-end — AWS GuardDuty / Security Hub / Macie / Inspector / IAM Access Analyzer / KMS / Secrets Manager / WAF (primary), with working knowledge of Microsoft Defender for Cloud / Sentinel and GCP Security Command Center. Lead Kubernetes and container security across managed offerings (EKS preferred; AKS, GKE accepted), including admission control, image signing (Sigstore/Cosign), runtime threat detection (Falco or equivalent), and Pod Security Standards enforcement. Drive supply-chain security to SLSA-aligned maturity: signed builds, attested artifacts, dependency provenance, and verified deploys. Own Regulated & Pharma-Specific Controls Engineer controls that satisfy GxP, 21 CFR Part 11, Annex 11, HIPAA, GDPR, and the client's global information security standards — without slowing delivery teams down. Design continuous compliance evidence pipelines that auto-generate audit artifacts for FDA, EMA, and internal QA inspections, replacing manual screenshotting and ticket-based attestations. Partner with Computer System Validation (CSV) and Computer Software Assurance (CSA) teams to align DevSecOps tooling with validated-state expectations for clinical, manufacturing, and pharmacovigilance systems. Champion data protection for sensitive scientific IP, clinical trial data, and patient-adjacent datasets — tokenization, encryption strategy, and least-privilege access across cloud data services (e.g., S3 / Redshift / RDS / Lake Formation on AWS, or equivalents on Azure and GCP). Drive Detection, Response & Resilience Engineer detection-as-code and response automation in collaboration with the client SOC; tune findings, suppress noise, and ensure every signal is actionable. Run blameless postmortems for security incidents and near-misses; convert lessons into durable engineering improvements. Establish security SLOs and meaningful metrics — mean time to remediate, control coverage, drift, and developer-impacting friction. Influence Across Client and Practice Build trust with the client's senior security, platform, and quality leadership; become the person they call before launching a new initiative. Contribute to Newpage's internal DevSecOps practice: reusable accelerators, case studies, hiring loops, and the next generation of senior engineers across the company. Qualification & Experience8+ years of professional experience in security engineering, platform engineering, or SRE, with at least 4 years leading DevSecOps initiatives at scale. Deep, current expertise with at least one major public cloud at production scale — AWS is strongly preferred (you have personally designed and operated multi-account environments with 50+ accounts); Azure or GCP at equivalent depth will be considered. Working familiarity with at least one additional cloud beyond your primary — enough to design controls that translate cleanly across providers. Strong hands-on coding skills in at least one of Python, Go, or TypeScript, and fluency in infrastructure-as-code with Terraform (cloud-agnostic mastery preferred; CDK, Bicep, or Pulumi also welcome). Demonstrable experience embedding security into CI/CD pipelines and developer workflows for engineering organizations of 200+ developers. Working knowledge of Kubernetes security on at least one managed offering (EKS preferred; AKS or GKE accepted) — including network policy, admission control, and supply-chain controls. Track record of operating in a regulated industry — pharma, healthcare, financial services, or critical infrastructure — and translating compliance frameworks into engineering controls. Excellent written and verbal communication skills; comfortable presenting to a client CISO one day and pairing with a junior engineer the next. Nice to have Direct experience with pharma or life-sciences workloads: GxP, 21 CFR Part 11, Annex 11, CSV/CSA, pharmacovigilance systems, or clinical data platforms. Nice to have Exposure to threat modeling frameworks (STRIDE, PASTA), MITRE ATT&CK, and threat-informed defense. Nice to have Experience with policy-as-code (OPA/Rego, Cedar) and continuous compliance platforms (Wiz, Prisma Cloud, Orca, Drata, Vanta) at enterprise scale. Nice to have Hands-on with secret management (HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, or GCP Secret Manager) and zero-trust networking patterns. Nice to have Relevant certifications such as AWS Security Specialty (preferred), Azure Security Engineer Associate, Google Professional Cloud Security Engineer, CISSP, CCSP, OSCP, or GIAC GCSA — credentials are a signal, not a substitute for evidence. Nice to have Familiarity with AI/ML pipeline security and the emerging risks around generative AI in regulated environments.

ResponsibilitiesBuild and implement AI/ML solutions to automate processes and generate business insights. Perform data collection, cleaning, preprocessing, and feature engineering on large datasets. Build, train, evaluate, and optimize machine learning and AI models to improve accuracy and performance. Conduct exploratory data analysis (EDA) to identify trends, patterns, and business opportunities. Work with Generative AI, LLMs, and NLP models where applicable to solve business problems. Collaborate with Product, Engineering, and Business teams to deliver data-driven and AI-powered solutions. Develop data pipelines and workflows for model training, validation, deployment, and monitoring. Create dashboards, reports, and visualizations to communicate insights to stakeholders. Stay updated on the latest advancements in Data Science, Artificial Intelligence, and Machine Learning. Design, develop, and deploy predictive models for forecasting, classification, regression, and business optimization use cases.Qualification & ExperienceStrong expertise in Python and SQL. Hands-on experience with machine learning libraries such as Scikit-learn, Pandas, NumPy, XGBoost, TensorFlow, or PyTorch. Strong understanding of statistics, probability, predictive modeling, and AI concepts. Experience with regression, classification, clustering, forecasting, and model evaluation methodologies. Experience working with NLP, LLMs, Generative AI, or AI-powered applications. Proficiency in feature engineering, model tuning, and performance optimization. Experience deploying and monitoring ML/AI models in production environments. Strong analytical thinking, problem-solving, and communication skills. Experience with time-series forecasting, recommendation systems, anomaly detection, or financial modeling.Familiarity with LLM frameworks and tools such as LangChain, LangGraph, OpenAI APIs, or similar AI ecosystems. Experience with MLOps tools and model deployment frameworks. Familiarity with data visualization tools such as Power BI, Tableau, or Looker. Experience working in fast-paced startup environments. Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, AI/ML, or a related field.

ResponsibilitiesDesign, develop and support applications and systems under the team’s scope Write clean, maintainable, performant, and well-tested code to implement new features and fix bugs Write unit tests, integration tests, and work with QA to coordinate timely regression for new features Monitor live system metrics, respond to alerts, and troubleshoot production issues Investigate and understand existing system technical functionality and propose technical improvements Collaborate with Product, Design, and Business stakeholders to plan and prioritize new feature development Communicate changes in project estimates, dependencies, and blockers with affected stakeholders quickly Own feature development and projects through design, development, testing, and release to production Understand and improve the scalability, maintainability, availability, and visibility of applications Document newly implemented technologies and application functionality Qualification & ExperienceDegree in Computer Science or related field, or equivalent experience Solid understanding of fundamental web technologies such as HTTP, REST, AJAX and JSON. Strong proficiency in HTML, CSS and JavaScript / ES6, including DOM manipulation and the JS object model Solid understanding of database principles, particularly how to query and update SQL databases Thorough understanding of REST principles and best practices of building and using RESTful APIs Thorough understanding of core design principles and common design patterns of Angular Experience with common front-end development tools such as Vite, NPM, Yarn, etc. Experience with GraphQL is a big plus

ResponsibilitiesArchitect, build, and maintain state-of-the-art Enterprise Data Warehouse / Lakehouse solutions that serve both batch and near-real-time analytics use cases Design and implement robust ETL / ELT pipelines using Python and Apache Airflow (or modern orchestration equivalents) Develop and operate real-time data streaming and processing platforms using open-source technologies such as Apache Kafka, Apache Spark Streaming / Structured Streaming, Flink, or equivalent Handle sensitive financial, production, and customer data systems while strictly adhering to SOX controls, segregation of duties, change management, and audit requirements Partner closely with business sponsors, product managers, manufacturing engineers, service operations, finance, and IT/security teams to gather requirements, scope projects, and deliver high-quality solutions quickly Communicate complex technical concepts and business impact effectively through written documentation, verbal discussions, architecture diagrams, and executive-level presentations (360-degree communication) Define, enforce, and continuously improve engineering standards, coding best practices, testing methodologies, CI/CD patterns, monitoring & alerting, and quality assurance processes Actively participate in design reviews, code walkthroughs, and pull request reviews across the team Stay current with evolving open-source technologies and recommend adoption when they provide meaningful differentiation or operational efficiency Provide global 24×7 data support on a rotating basis (on-call) and own ETL / streaming pipeline health monitoring, alerting, and incident resolutionQualification & Experience6+ years of professional experience as a Data Engineer, Backend Engineer, or ETL developer building large-scale data platforms Strong proficiency in Python for data engineering (pandas, PySpark, SQLAlchemy, etc.) Deep hands-on experience designing and operating Airflow DAGs in production at scale Production experience with at least one distributed streaming system (Kafka, Kafka Streams, Spark Streaming, Flink, Pulsar, etc.) Solid understanding of data modeling for analytical workloads Experience building and operating systems under SOX compliance or similarly regulated environments (change control, audit trails, separation of duties, etc.) Strong SQL skills and understanding of distributed query engines Experience with containerization (Docker) and orchestration (Kubernetes / ECS) is highly desirable Excellent communication skills — able to explain technical trade-offs to engineers and business value to non-technical stakeholders

ResponsibilitiesCreate and maintain/deploy custom apps with AppEngine Studio Design and implement table structure Implement integration to source systems via REST, JDBC load, etc. Design and build/adjust custom flow with Flow Designer, i.e. approvals, task, notifications Adjusting Service Portal, i.e. Ticket page, approval and custom widget Adjusting configurable workspace Server and client side scripting, i.e. validations, automation, rules and logic Adjusting ACLs (Access Control List) Set up infrastructure components such as email, LDAP Apply ServiceNow best practices Qualification & ExperienceUnderstand ServiceNow platform and specifically AppEngine Must: Certification as Certified System Administrator (CSA) Certification as Certified Application Developer (CAD) Nice to have: Hands-on experience with ServiceNow AI capabilities 5 years experience in JavaScript or equivalent 3 years experience as Administrator of ServiceNow platform and AppEngine development

ResponsibilitiesLinux Device Driver Development & Debugging Develop and maintain drivers for HSIO subsystems (PCIe, USB, UFS, Ethernet, etc.) Work on feature enablement, bug fixing, and performance optimizations New Hardware Bring-UpParticipate in pre-silicon validation, FPGA/Emulator environments, and first-silicon bring-up Debug using JTAG, serial consoles, and kernel logging tools Read and analyze board schematics to support hardware-software integration Linux Internals & Kernel SubsystemsContribute to kernel migration and version upgrades for new SoCs Work with Linux kernel frameworks like power management, interrupt controller (GIC), clock/PLL, memory, and HSIO IP subsystems. Bootloader & FirmwareDevelop, debug, and customize bootloaders (U-Boot, Coreboot, ATF) Enable low-level initialization and bring-up of SoCs System Performance & PowerSupport power and performance validation activities Contribute to enabling features like DVFS, Runtime PM, CPUIdle, and suspend/resume Upstreaming & CollaborationPrepare and test patches for kernel upstreaming Collaborate with open-source community and global stakeholdersQualification & Experience3–6 years of hands-on experience in Linux kernel and device driver development Strong C programming skills and debugging expertise Exposure to hardware bring-up on new boards/SoCs Ability to read and interpret board schematics. Experience with bootloaders (U-Boot, Coreboot, ATF) Familiarity with Linux kernel internals (memory management, scheduling, interrupts, device model) Experience in debugging using JTAG, logic analyzers, or oscilloscopes Good knowledge of source control systems (Git/Gerrit/GitHub) and kernel patch workflow. Desirable / Good-to-Have Skills: Experience with HSIO IPs (PCIe, USB, UFS, Ethernet, etc.) Contributions to upstream Linux kernel or open-source projects Exposure to pre-silicon validation environments (FPGA/Emulators) Familiarity with test automation frameworks (Python, shell scripting). Soft Skills: Strong analytical and problem-solving skills Good communication and teamwork in cross-functional, global teams Ability to work in a fast-paced environment and take ownership

ResponsibilitiesDevelop, enhance, and maintain Java utilities/services supporting GCMA operational workflows (e.g., onboarding, request processing, environment tasks) Execute .NET to Java utility conversion and perform Java refactoring to improve maintainability and performance Support platform modernization work including Splunk to Kafka migration updates for utilities where applicable. Bring utility components under CMDB governance and improve standardization/documentation for run/support readiness Partner with Vault configuration and release teams to align utility behavior with evolving Veeva/GCMA functionality and newer feature releases. Support release execution by triaging issues, collaborating with QA, and ensuring deployment readiness (including dependencies that can impact scope/cut decisions). Participate in production support knowledge transfer and operational procedures (runbooks, scripts, verification via logs) as part of BAU/release readinessQualification & Experience 3–6+ years of hands-on Java development experience (building services/utilities; refactoring and modernization work).  Experience with utility modernization and refactoring (including legacy tech migration such as .NET ? Java)  Experience with event/logging pipeline changes such as Splunk ? Kafka updates in supporting utilities. Strong troubleshooting skills using logs and controlled verification steps to validate changes and support releases Ability to work effectively with cross-functional teams (Vault dev/config, QA, release management) in a release-driven environment

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 fullstack applications, APIs, agents, workflows, and similar systems using frameworks such as Next.js, React, FastAPI, Fastify, FastMCP, and Hono. Architect and ship production-grade agentic applications using LangGraph, 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, ProductionizeSpin 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-fullstack 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 LangGraph, 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, FastAPI 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. Public writing, talks, or threads about building with AI. MLOps and model serving experience (BentoML, MLflow, Vertex AI, SageMaker). Streaming and batch ingestion pipelines (Spark, Airflow, Beam, Glue). Healthcare or life sciences domain exposure. AWS Professional certification or other relevant industry certifications.