{"id":1903201,"url":"https://alion.io/job/bjak-llm-application-engineer-12","title":"LLM Application Engineer","company":{"id":12599,"name":"Bjak","domain":"bjak.my","url":"https://alion.io/company/bjak","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":100,"open_postings":80,"ghost_share":0,"stale_share":0.05,"repost_share":0,"time_to_fill_p50_days":8,"computed_at":"2026-10-10T05:45:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":90000,"max_usd":236000,"period":"year","method":"role_country_seniority_unknown","sample_n":131},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Context Engineering","optional":false},{"name":"LLM","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tool Use","optional":false},{"name":"JAX","optional":true},{"name":"OpenAI","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true}],"status":"live","first_seen_at":"2026-09-28T04:21:53Z","employer_posted_date":"2026-09-28","last_verified_at":"2026-10-11T02:20:42Z","board_verified":true,"closed_at":null,"days_open":13,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":13},"description":"About ActAI\nThere are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.\nOur product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. We believe products will greatly reduce hallucinations.\nOur objective is to organise anyone's life, allowing us all to spend time on valuable and meaningful things.\nAbout the Role\nAs an LLM Application Engineer, you will build the intelligence layer that powers ActAI's AI experiences.\nYou will work at the intersection of LLMs, software engineering, and product - designing agent workflows, improving model behaviour, and turning AI capabilities into reliable user experiences.\nYou will own problems end-to-end, from understanding user needs, designing Agentic workflows, integrating models and tools, building evaluation system and continuously improving AI behaviour in production.\nFocus\nBuild and ship LLM-powered applications and AI agent workflows\n\nDesign systems for reasoning, planning, memory, tool uuse and multi-step execution\n\nBuild reliable orchestration pipelines that turn probabilistic model outputs into predictable, observable, and safe actions\n\nIntegrate LLMs with APIs, databases, search, internal services, and external tools.\n\nDevelop prompting, context engineering, structured outputs, tool-calling, and other techniques to improve model behaviour\n\nBuild evaluation frameworks and datasets to measure AI quality, reliability, and regressions\n\nDebug AI systems across the entire stack-from model behaviour and prompts to orchestration, backend services, and product UX\n\nOptimise AI systems for quality, latency, and cost\n\nWork closely with product and engineering teams to turn ambiguous product problems into working AI solutions\n\nEstablish production practices for observability, tracing, experimentation, evaluation, and continuous improvement\n\nTech Stack\nPython\n\nLLM APIs and model providers, including OpenAI-compatible APIs and open-weight models\n\nAgent frameworks and orchestration systems\n\nVector databases and retrieval systems\n\nBackend services, APIs, and distributed systems\n\nPyTorch / JAX\n\nIdeal Experience\nStrong software engineering fundamentals with experience building AI-powered applications\n\nHands-on experience with LLMs, generative AI, or agent-based systems\n\nExperience designing prompts, workflows, evaluations, or AI behaviour\n\nAbility to write clean, production-quality code\n\nComfortable working across abstraction layers (model → system → product)\n\nStrong problem-solving skills in ambiguous, fast-moving environments\n\nBias toward shipping, iteration, and continuous improvement\n\nOutcomes\nAI features reach production quickly and deliver measurable user impact\n\nLLM-powered workflows are reliable, scalable, observable, and maintainable\n\nAI quality improves through systematic evaluation, experimentation, and iteration\n\nAI workflows become increasingly predictable, efficient, and cost-effective\n\nComplex AI capabilities are translated into simple, intuitive user 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