{"id":2702111,"url":"https://alion.io/job/exavalu-lead-ai-engineer-2","title":"Lead AI Engineer","company":{"id":3802017,"name":"Exavalu","domain":"exavalu.com","url":"https://alion.io/company/exavalu","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Zoho Recruit","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"remote","remote_scope":"unspecified","remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":127000,"max_usd":269000,"period":"year","method":"global_role_seniority_cell","sample_n":1119},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon EKS","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Lambda","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NLP","optional":false},{"name":"Pinecone","optional":false},{"name":"RAG","optional":false},{"name":"Weaviate","optional":false}],"status":"live","first_seen_at":"2026-10-16T11:27:46Z","employer_posted_date":"2026-10-16","last_verified_at":"2026-10-17T14:11:01Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"This is a remote position.\nOverview :\nBuild AI Systems (Core Responsibility)\nDesign and implement end-to-end AI/ML solutions including LLM-based applications\nBuild RAG pipelines using vector databases and enterprise data sources\nBuild machine learning models that automate their training, validation, monitoring, and retraining\nDevelop APIs and services to operationalize AI capabilities across the organization\nDevelop Data + AI Pipelines\nBuild ingestion for multimodal content and transformation pipelines for structured and unstructured data\nIntegrate AI workflows with enterprise systems (policy, claims, billing, etc.)\nEnsure data quality, traceability, reliability, and governance in all AI pipelines\nOperationalize Models (MLOps)\nImplement CI/CD for AI/ML workflows\nDeploy, monitor, and maintain models in production\nManage model versioning, performance monitoring, and retraining processes\nBuild on AWS\nDevelop solutions using: Amazon SageMaker, AWS Lambda, S3, Glue, EKS, and related services\nContribute to evolving use of AWS Bedrock\nApply Responsible AI Practices\nImplement guardrails for LLM-based systems (grounding, validation, safety)\nEnsure secure handling of sensitive data (PII, financial, etc.)\nBuild systems aligned with enterprise governance and compliance standards\nQualifications:\nRequired\n10+ years in software, data engineering, 5 years AI/ML engineering\nHands-on experience building production AI/ML systems\nExperience with RAG pipelines, LLMs, or NLP-based systems\nExperience with AWS Bedrock or similar GenAI platforms\nExperience with data pipelines and distributed systems\nExperience deploying and operating systems in AWS\nWorking knowledge of MLOps practices (CI/CD, monitoring, versioning)\nPreferred\nExperience with vector databases (Pinecone, Weaviate, etc.)\nExperience in regulated industries (insurance, finance, healthcare)\nExposure to microservices and containerized environments (Docker, Kubernetes)\nRequirements\nQualifications:\nRequired\n10+ years in software, data engineering, 5 years AI/ML engineering\nHands-on experience building production AI/ML systems\nExperience with RAG pipelines, LLMs, or NLP-based systems\nExperience with AWS Bedrock or similar GenAI platforms\nExperience with data pipelines and distributed systems\nExperience deploying and operating systems in AWS\nWorking knowledge of MLOps practices (CI/CD, monitoring, versioning)\nPreferred\nExperience with vector databases (Pinecone, Weaviate, etc.)\nExperience in regulated industries (insurance, finance, healthcare)\nExposure to microservices and containerized environments (Docker, Kubernetes)\nBenefits\nDiversity Inclusion:\nAt Exavalu, we are committed to building a diverse and inclusive workforce. We welcome applications for employment from all qualified candidates, regardless of race, color, gender, national or ethnic origin, age, disability, religion, sexual orientation, gender identity or any other status protected by applicable law. We nurture a culture that embraces all individuals and promotes diverse perspectives, where you can make an impact and grow your career.\nExavalu also promotes flexibility depending on the needs of employees, customers and the business. It might be part-time work, working outside normal 9-5 business hours or working remotely. 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