{"id":1376427,"url":"https://alion.io/job/meraki-labs-ai-engineer-backend-2","title":"AI Engineer - Backend","company":{"id":1312,"name":"Meraki Labs","domain":"merakilabs.com","url":"https://alion.io/company/meraki-labs","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":true,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Copilot","optional":false},{"name":"LLM","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reward Modeling","optional":false},{"name":"RLHF","optional":false},{"name":"Scale AI","optional":false},{"name":"HIPAA","optional":true},{"name":"RAG","optional":true},{"name":"Text-to-Speech","optional":true}],"status":"live","first_seen_at":"2026-09-28T06:49:20Z","employer_posted_date":null,"last_verified_at":"2026-09-28T06:49:20Z","board_verified":false,"closed_at":null,"days_open":21,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":21},"description":"Responsibilities:\nLaunch and scale high-quality AI agents.\nBuild and deploy voice AI for automating patient activation and outbound calls to insurance, vendors, and providers.\nDesign individual agents that orchestrate end-to-end workflows such as DME delivery, prior authorisations, and care coordination.\nBuild copilots that assist and nudge advocates in real time.\nBuild the eval and reinforcement learning pipeline.\nSet up the evaluation infrastructure that measures agent quality across every interaction.\nCapture the right data from product usage, build the feedback loop, and implement RL pipelines so that agent performance improves continuously with real-world usage.\nCreate the usage-driven product improvement flywheel.\nScale AI Nurse by composing multiple agents.\nBring together voice, workflow, and copilot agents into a unified AI Nurse experience.\nEnsure that as we scale, each agent reinforces the others through shared learning and a consistent improvement loop.\nShip fast and iterate with real users.\nDeploy to production, monitor how agents perform with actual patients, and improve based on real conversations.\nOwn the tight loop from usage data to system-level improvements.\nShape the technical roadmap.\nWork with the founders to decide what to build next.\nBring deep knowledge of what's possible with current AI capabilities and help us make smart bets on where the technology is heading.\nLay the foundation for scale.\nMake architectural decisions that will hold up as we grow from hundreds of patients to hundreds of thousands.\nDocument systems, establish best practices, and build with the next engineer in mind.\nRequirements:\nYou've built AI agents or LLM-powered systems from scratch and shipped them to production, not just demos, but systems handling real interactions with real users at scale.\nYou have hands-on experience building evaluation pipelines for AI systems, designing metrics, capturing signals from production usage, and using that data to systematically improve agent quality.\nYou have experience with reinforcement learning from human feedback (RLHF), reward modelling, or other feedback-driven improvement loops for LLM-based systems.\nYou've monitored and debugged AI systems in production, you know what it takes to keep agents reliable when they're interacting with real people.\nYou're comfortable with ambiguity.\nThe playbook doesn't exist yet, and you're excited to build it.\nNice to Have:\nExperience in healthcare, health tech, or regulated industries (HIPAA, PHI handling).\nFamiliarity with Medicare, insurance workflows, or clinical operations.\nExperience with voice AI systems, speech-to-text, text-to-speech, and real-time voice orchestration.\nBackground in RAG systems, vector databases, and knowledge retrieval pipelines.\nContributions to open-source AI projects or a portfolio of side projects that show your range.","description_format":"text","description_chars":2870,"description_truncated":false,"requirements":{"experience_years_min":null,"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":["Angel Networks & Startup Accelerators"],"lifecycle":[{"event":"open","at":"2026-09-28T06:49:20Z"}],"visa":[],"liveness":{"score":56,"band":"ok","label":"Likely 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