{"id":1467852,"url":"https://alion.io/job/weekday-staff-ml-engineer-2","title":"Staff ML Engineer","company":{"id":7097,"name":"Weekday","domain":"weekday.works","url":"https://alion.io/company/weekday","size_band":"51-200","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":81,"open_postings":87,"ghost_share":0,"stale_share":0.966,"repost_share":0,"time_to_fill_p50_days":5,"computed_at":"2026-10-06T05:45:30Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":37000,"max_usd":88000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":11},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"Arize Phoenix","optional":false},{"name":"Claude","optional":false},{"name":"Claude Code","optional":false},{"name":"Cursor","optional":false},{"name":"Function Calling","optional":false},{"name":"Gemini","optional":false},{"name":"GraphRAG","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LangChain","optional":false},{"name":"Langfuse","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"Model Distillation","optional":false},{"name":"PEFT","optional":false},{"name":"PoC Library","optional":false},{"name":"QLoRA","optional":false},{"name":"RAG","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tool Use","optional":false},{"name":"Transformers","optional":true}],"status":"closed","first_seen_at":"2026-09-29T15:18:39Z","employer_posted_date":"2026-09-29","last_verified_at":"2026-10-05T16:24:27Z","board_verified":false,"closed_at":"2026-10-05T16:24:27Z","days_open":6,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":6},"description":"'\n: - ( - )\nExperience: 13+ yrs\nLocation: Bengaluru\nJob Type: Full-time\nWe are looking for an experienced Staff ML Engineer - Generative AI to design, build, and scale production-grade GenAI applications and intelligent software systems. The role combines hands-on engineering, AI architecture, technical leadership, and end-to-end ownership of enterprise AI solutions.\nThe ideal candidate will have strong experience taking GenAI applications beyond prototypes into production, with a focus on reliability, evaluation, observability, security, cost optimisation, user trust, adoption, and measurable business impact.\nRequirements\nKey Responsibilities\nDesign, develop, and launch production-grade GenAI applications including assistants, copilots, document intelligence, workflow automation, and decision-support solutions.\nIdentify high-impact opportunities where AI can improve productivity, service quality, operational efficiency, customer experience, or business outcomes.\nTake GenAI applications from concept and experimentation through production deployment and ongoing optimisation.\nLead hands-on technical execution across application architecture, model selection, prompting, retrieval, orchestration, APIs, data pipelines, and user experiences.\nArchitect scalable LLM applications using RAG, agentic workflows, tool use, structured outputs, grounding, and orchestration.\nEvaluate and select appropriate frontier models, open-source models, smaller task-specific models, fine-tuned models, or deterministic approaches based on business requirements.\nEstablish practical evaluation frameworks covering accuracy, relevance, groundedness, safety, latency, cost, user trust, adoption, and business impact.\nBuild production capabilities for observability, monitoring, versioning, fallback mechanisms, privacy, security, reliability, and operational ownership.\nAnalyse production feedback and continuously improve AI application quality, performance, reliability, and user experience.\nWork with cross-functional stakeholders to define requirements, establish success criteria, and measure real-world impact.\nStay current with emerging GenAI technologies and pragmatically evaluate techniques that improve quality, speed, scalability, or cost efficiency.\nContribute to engineering standards, technical architecture decisions, AI development practices, and responsible AI implementation.\nMentor engineers and provide technical leadership across complex AI application initiatives.\nWhat Makes You a Great Fit\n13+ years of experience building applied AI/ML-based intelligent software systems, with strong hands-on engineering expertise.\n3+ years of practical GenAI application experience, including production applications used by real users at meaningful scale.\nProven experience taking GenAI solutions from PoC/prototype to production, with ownership of reliability, launch quality, cost, user feedback, adoption, and measurable impact.\nStrong understanding of modern LLM application architectures including RAG, agents, tool use, structured outputs, retrieval, grounding, and orchestration.\nExperience with LangGraph, LangChain, LlamaIndex, and LLM APIs such as GPT, Claude, or Gemini.\nStrong programming and software engineering capabilities, with the ability to build production-ready AI applications rather than only prototypes.\nExperience implementing evaluation and observability frameworks using tools such as Langfuse, Arize, or similar platforms.\nStrong understanding of enterprise AI requirements including security, privacy, reliability, monitoring, cost management, and user trust.\nExperience with AI-native development tools such as Cursor, Claude Code, or similar tools is preferred.\nStrong architectural judgement with the ability to balance model capabilities, application complexity, performance, cost, and reliability.\nExperience with advanced AI techniques such as GraphRAG, long-context architectures, model routing, caching, cascades, PEFT/LoRA/QLoRA, knowledge distillation, or open-source model deployment is an advantage.\nStrong analytical, problem-solving, communication, and cross-functional collaboration skills.\nAbility to operate effectively in ambiguous, fast-moving environments and take end-to-end ownership of complex technical initiatives.\nStrong interest in building trustworthy, scalable, measurable, and production-ready AI 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