{"id":4195344,"url":"https://alion.io/job/geeksoft-consulting-ai-engineer-lead","title":"AI Engineer Lead","company":{"id":20414,"name":"GeekSoft Consulting","domain":"geeksoftconsulting.com","url":"https://alion.io/company/geeksoft-consulting","size_band":"201-500","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Zoho Recruit","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"contractor","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Jose, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"AI Agents","optional":false},{"name":"Airflow","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AutoGen","optional":false},{"name":"CI/CD","optional":false},{"name":"Datadog","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"GitLab CI","optional":false},{"name":"Google ADK","optional":false},{"name":"Grafana","optional":false},{"name":"IAM","optional":false},{"name":"Incident Management","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"SQL","optional":false},{"name":"Vertex AI","optional":false},{"name":"Zero Trust","optional":false}],"status":"live","first_seen_at":"2026-11-12T20:27:47Z","employer_posted_date":"2026-11-12","last_verified_at":"2026-11-12T20:27:47Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Help design, build and continuously improve the clients online platform. \nResearch, suggest and implement new technology solutions following best practices/standards.\nTake responsibility for the resiliency and availability of different products.\nBe a productive member of the team.\nRequirements\n8+ years of experience in Software Engineering, ML Engineering, MLOps, or DevOps, including 2+ years in a technical leadership role.\nExperienced ML/MLOps Technical Leadwith strong expertise in machine learning operations, GenAI, Agentic AI, and production ML infrastructure. \nLead globally distributed engineering teams, drive end-to-end ML lifecycle management, and ensure scalable, secure, and reliable AI model deployment, evaluation, and monitoring.\nDefine technical strategy and roadmap for ML sustenance across multiple services.\n\nAct as the primary onsite technical contact and lead globally distributed engineering teams.\n\nDrive architecture reviews, define Service-Level Objectives (SLOs), and coordinate with client teams for AI agent onboarding.\n\nStandardize model evaluation processes and mentor engineering teams.\n\nDesign, develop, and maintain automated Continuous Training (CT), data ingestion, and feature engineering pipelines.\n\nWork with MLOps frameworks and tools such as Kubeflow, MLflow, and Apache Airflow.\n\nIntegrate datasets from distributed SQL systems and messaging platforms such as Apache Kafka.\n\nImplement model, prompt, and evaluation configuration versioning, experiment tracking, and hyperparameter tuning.\n\nBuild isolated agent execution environments using Kubernetes and Docker.\n\nDevelop evaluation pipelines, synthetic datasets, golden datasets, and simulated execution traces.\n\nEstablish quality dashboards and automated monitoring using metrics such as Accuracy, AUC, ROUGE, and Trajectory Exact Match.\n\nImplement alerting mechanisms to detect model drift, hallucinations, and quality degradation.\n\nEvaluate and monitor automated assessment frameworks, including LLM-as-a-Judge.\n\nBuild and maintain CI/CD pipelines for automated ML model deployment and rollback.\n\nImplement monitoring across metrics, logs, and traces to ensure production reliability and low-latency model serving.\n\nHandle production incidents, troubleshooting, traffic rollouts, and root-cause analysis.\n\nOptimize system performance, resolve capacity bottlenecks, and improve MTTR.\n\nImplement data isolation mechanisms for sensitive and multi-tenant environments.\n\nEnforce IAM, RBAC, encryption, and Zero Trust security principles.\n\nEnsure compliance with data retention, access control, and deletion policies.\n\nDevelop AI-driven operational automation, including monitoring agents, automated incident triage, and documentation synchronization.\n\nOptimize infrastructure, compute, and token costs across model serving and evaluation workloads.\n\nStrong expertise in GCP MLOps services, particularly Vertex AI and Kubeflow, or equivalent tools such as MLflow and Apache Airflow.\n\nProficiency in Python or Go, distributed systems, and large-scale data processing.\n\nHands-on experience with Kubernetes, Docker, containerized deployments, and CI/CD tools such as Jenkins or GitLab CI.\n\nStrong understanding of end-to-end ML pipelines covering data ingestion, model training, evaluation, deployment, and continuous monitoring.\n\nExperience in production operations, SLO definition, monitoring, alerting, incident management, and root-cause analysis.\n\nProven experience leading distributed teams and managing technical and business stakeholders.\n\nKnowledge of LLMs and Agentic AI frameworks such as LangChain, AutoGen, LlamaIndex, or Google Agent Development Kit (ADK).\n\nExperience with ML and GenAI evaluation metrics, including BLEURT, ROUGE, Precision, Recall, AUC, and Trajectory Exact Match.\n\nExperience handling sensitive data such as PII/PHI with strong encryption and access controls.\n\nKnowledge of model monitoring, data/concept drift detection, A/B testing, and quality dashboards using Grafana or Datadog.\n\nExperience with production support, ML sustenance, SRE, service transition, and knowledge-transfer programs.\n\nBenefits\nA challenging, innovating environment. \nOpportunities for learning where needed.","description_format":"text","description_chars":4208,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":2,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Consulting & Digital 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