{"id":3233539,"url":"https://alion.io/job/arintra-staff-ai-engineer","title":"Staff AI Engineer","company":{"id":424,"name":"Arintra","domain":"arintra.com","url":"https://alion.io/company/arintra","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"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":36000,"max_usd":83000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":13},"experience_years_min":9,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"AI Agents","optional":false},{"name":"Fine-tuning","optional":false},{"name":"HIPAA","optional":false},{"name":"LLM","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"RAG","optional":false}],"status":"live","first_seen_at":"2026-10-25T09:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-25T09:35:00Z","board_verified":false,"closed_at":null,"days_open":4,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":4},"description":"As a Staff Engineer, you'll drive architecture and design for distributed systems that operate at scale, while still rolling up your sleeves as an IC. You'll work closely with product and business teams, mentor younger engineers, and solve 0-1 challenges that push both technology and the business forward. If you want to build, scale, and leave a tangible mark in healthcare AI, this role is for you.\n\nYour responsibilities will span the following functions:\n\nTechnical Strategy and Architecture: \nDefine a technical roadmap for evolving from prompt-based systems to RAG and AI agents.\nDrive architectural decisions for scalable, cost-effective Gen AI solutions in healthcare.\nOversee model evaluation, selection, and deployment strategies across multiple medical specialties.\nEnsure technical debt management and maintainable code standards across the team.\nLead technical design reviews and architecture discussions.\nStay current with Gen AI research and translate innovations into product capabilities.\n\nProduct and Project Delivery: \nCollaborate with Product, Clinical, and Business teams to translate requirements into technical solutions.\nManage project timelines, resource allocation, and delivery commitments for Gen AI initiatives.\nDrive cross-functional collaboration to ensure seamless integration of AI capabilities.\nOversee A/B testing, experimentation, and data-driven decision making for AI features.\nEnsure compliance with healthcare regulations (HIPAA) and coding standards (ICD-10 CPT).\n\nOperational Excellence and Cost Management: \nImplement monitoring, observability, and SLA management for production Gen AI systems.\nDrive cost optimization initiatives for LLM usage, infrastructure, and data processing\nEstablish best practices for Gen AI MLOps, including model versioning and deployment pipelines.\nEnsure system reliability, scalability, and performance optimization.\nLead incident response and post-mortem processes for AI system issues.\n\nStrategic Planning: \nPartner with leadership to define Gen AI strategy and competitive positioning.\nEvaluate build vs buy decisions for Gen AI capabilities and tooling.\nDrive innovation initiatives and proof-of-concept development for new AI applications.\nRepresent engineering in executive discussions about AI roadmap and investment priorities.\n\nRequirements: \n3+ years of hands-on experience with Deep Learning, Large Language Models, and Gen AI applications.\nDeep understanding of RAG architectures, vector databases, and AI agent frameworks.\nExperience with production Gen AI systems: deployment, monitoring, and cost optimization.\nKnowledge of model evaluation, fine-tuning, and prompt engineering best practices.\nUnderstanding of MLOps practices for LLM deployments and model lifecycle management.\nSkills\nGenAI, LLM, MLOps, RAG, generative AI, large language models, retrieval","description_format":"text","description_chars":2847,"description_truncated":false,"requirements":{"experience_years_min":9,"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":["Medical AI","Revenue Cycle & Medical 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