{"id":3810815,"url":"https://alion.io/job/moveworks-senior-software-engineer-2","title":"Senior Software Engineer","company":{"id":840,"name":"Moveworks","domain":"moveworks.com","url":"https://alion.io/company/moveworks","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Backend","role_family":"Backend","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"Claude Code","optional":false},{"name":"Context Engineering","optional":false},{"name":"Cursor","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"Go","optional":false},{"name":"GraphQL","optional":false},{"name":"Hybrid Search","optional":false},{"name":"JavaScript","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenAI Codex","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"React.js","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Search","optional":false},{"name":"Structured Outputs","optional":false},{"name":"TypeScript","optional":false},{"name":"Windsurf","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"CI/CD","optional":true},{"name":"GCP","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"Kubernetes","optional":true},{"name":"LLM","optional":true},{"name":"Multi-Agent Systems","optional":true}],"status":"live","first_seen_at":"2026-11-05T14:35:00Z","employer_posted_date":null,"last_verified_at":"2026-11-05T14:35:00Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"As a Senior Software Engineer with AI-native development experience, you will design, build, and operate next-generation AI-powered applications and platforms. You will combine strong full-stack software engineering fundamentals with hands-on expertise in modern AI/ML technologies to build production-grade experiences powered by LLMs, agents, retrieval, and intelligent automation. You will work across the technology stack—from user experiences and APIs to distributed services, data and retrieval systems, AI/ML workflows, and cloud infrastructure. You will be expected to use AI-native development practices to accelerate engineering productivity while maintaining high standards for scalability, reliability, security, and quality. This role is ideal for an engineer who enjoys solving complex problems end-to-end and is excited about applying AI as a core engineering capability, not simply as an add-on to traditional software.\n\nResponsibilities: \nDesign and build end-to-end full-stack applications, including frontend experiences, backend services, APIs, data layers, and cloud-native infrastructure.\nBuild production-grade AI/ML-powered capabilities using LLMs, RAG, embeddings, semantic search, agentic workflows, and intelligent decision-making.\nDesign and implement agentic architectures, including tool calling, orchestration, planning loops, memory, context management, and failure recovery.\nDevelop reliable AI-powered APIs and services that integrate frontier models and enterprise data securely and efficiently.\nBuild retrieval and grounding pipelines using vector search, hybrid search, semantic retrieval, re-ranking, and contextual enrichment.\nEstablish evaluation and observability mechanisms to measure AI quality, accuracy, latency, cost, reliability, and safety.\nTake features from concept and prototype through production deployment and ongoing operation, with ownership of quality and reliability.\nWork with product, design, platform, data, security, and other engineering teams to translate ambiguous problems into scalable technical solutions.\nContribute to architecture and design decisions, code reviews, engineering standards, and technical direction.\nMentor engineers and help raise the bar on full-stack engineering and production AI development practices.\nDesign and develop scalable, maintainable frontend applications and backend services using REST/GraphQL APIs, microservices, event-driven services, and distributed systems.\nWork with modern frontend technologies such as React, TypeScript, JavaScript, or equivalent frameworks.\nBuild cloud-native applications with a strong focus on scalability, performance, reliability, and security.\nOwn software delivery across development, testing, deployment, monitoring, and production operations.\nBuild applications leveraging LLMs, generative AI, embeddings, RAG, semantic search, and agentic workflows.\nIntegrate frontier AI models and SDKs such as OpenAI, Anthropic, Google, or equivalent platforms.\nApply prompt engineering, structured outputs, function/tool calling, context engineering, and model selection to real-world applications.\nDesign agent workflows that can reason, use tools, retrieve information, execute actions, and recover from failures.\nBuild AI evaluation frameworks and automated tests to measure model and application quality.\nBalance model capability, accuracy, latency, scalability, and cost when selecting and integrating AI models.\nApply AI safety, security, privacy, governance, and guardrail practices to production AI systems.\nExplore and adopt emerging AI technologies and rapidly turn promising capabilities into production-ready solutions.\n\nAI-Native Engineering Practices: \nUse AI-assisted development tools and coding agents such as Claude Code, Codex, Cursor, Windsurf, or equivalent tools as part of the software development lifecycle.\nApply AI to improve engineering productivity across coding, testing, debugging, documentation, code review, and system design.\nDevelop effective workflows for collaborating with coding agents while maintaining engineering quality and accountability.\nHelp establish best practices for AI-native software development across the engineering organization.\n\nRequirements: \n5+ years of software engineering experience, or equivalent practical experience.\nExperience designing and delivering production software systems.\nExperience building or integrating AI/ML-powered applications in a production or near-production environment.\nModern AI experience: LLMs, RAG, embeddings, vector search, agentic workflows, model evaluation, or AI observability.\nStrong programming experience in Python and/or Java, Go, or a similar language.\nCloud-native technologies, distributed systems, APIs, databases, and scalable architectures.\nBachelor's or master's degree in computer science, artificial intelligence, machine learning, or a related technical discipline, or equivalent practical experience.\nCybersecurity or security-product experience is a plus.\nStrong understanding of software engineering fundamentals, data structures, algorithms, design patterns, APIs, and distributed systems.\nHands-on experience developing full-stack applications, with strength in both frontend and backend engineering.\nStrong programming experience in one or more of Python, Java, Go, TypeScript, JavaScript, or similar languages.\nExperience with modern frontend development, preferably React and TypeScript or equivalent technologies.\nExperience designing and building cloud-native applications, scalable APIs, microservices, databases, and distributed systems.\nHands-on experience building or integrating AI/ML-powered applications in production or near-production environments.\nPractical understanding of modern AI concepts, including LLMs, embeddings, RAG, vector databases/search, semantic search, agents, tool calling, and model evaluation.\nExperience integrating one or more frontier AI model platforms/SDKs such as OpenAI, Anthropic, or Google.\nAbility to take an AI/ML prototype and turn it into a reliable, scalable, maintainable production solution.\nStrong debugging, problem-solving, and system-design skills.\nStrong communication and collaboration skills with the ability to work effectively across engineering, product, design, data, and platform teams.\nDemonstrated ownership of technical decisions and a track record of improving code quality and engineering practices.\n\nNice to Have: \nExperience designing multi-agent systems and agent orchestration frameworks.\nExperience with AI evaluation, observability, guardrails, and responsible AI practices.\nExperience with vector databases, hybrid retrieval, re-ranking, knowledge graphs, or enterprise search.\nExperience with ML pipelines, MLOps, model monitoring, or inference optimization.\nExperience with cloud platforms such as AWS, Azure, or GCP.\nExperience with Kubernetes, containers, CI/CD, and infrastructure-as-code.\nExperience with AI coding agents such as Claude Code, Codex, Cursor, or Windsurf.\nContributions to open-source AI/ML or developer tooling projects.\nExperience working on cybersecurity, identity, risk, enterprise SaaS, or other complex domain platforms is a plus.\n\nWhat Success Looks Like: \nIn this role, you will be successful when you can:\nBuild end-to-end: Take a product requirement from UI and API design through AI services, data, deployment, and production operations.\nBuild AI-native: Understand when and how to apply LLMs, agents, retrieval, and other AI capabilities to solve problems effectively.\nEngineer for production: Move beyond prototypes and deliver systems that are scalable, observable, secure, reliable, and maintainable.\nThink across the stack: Understand the trade-offs between user experience, application architecture, data, AI models, infrastructure, cost, and performance.\nRaise the engineering bar: Influence architecture, mentor engineers, and establish effective practices for building AI-native software.\nSkills\nEmbeddings, Go, Java, JavaScript, LLM, OpenAI, Python, RAG, React, TypeScript, Vector Search","description_format":"text","description_chars":8043,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Service & Asset Management","Conversational AI","AI Agents"],"lifecycle":[{"event":"open","at":"2026-11-05T14:35:00Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring 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