{"id":2568104,"url":"https://alion.io/job/hcltech-senior-technical-manager-13","title":"Senior Technical Manager","company":{"id":225,"name":"HCLTech","domain":"hcltech.com","url":"https://alion.io/company/hcltech","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"Leadership","role_family":"Leadership","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chicago, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":129000,"max_usd":262000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1203},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Embeddings","optional":false},{"name":"GCP","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenAI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Vertex AI","optional":false},{"name":"LLM Guardrails","optional":true}],"status":"live","first_seen_at":"2026-10-14T22:22:56Z","employer_posted_date":"2026-10-14","last_verified_at":"2026-10-15T07:43:30Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Job Summary\nJob Title: Senior Technical Manager - Enterprise AI\nRole Overview\nWe are seeking a Senior Technical Manager to lead the client’s Enterprise AI portfolio across multiple projects and teams. The role will own technical delivery, guide solution architecture, and work closely with business stakeholders and senior leadership to translate business priorities into secure, scalable AI solutions with measurable outcomes.\nKey Responsibilities\nManage the end-to-end AI project portfolio, including discovery, feasibility assessment, planning, development, deployment, and production support.\n\nPartner with business stakeholders to identify and prioritize AI use cases, define success criteria, and establish delivery roadmaps.\n\nLead cross-functional teams comprising AI/ML engineers, application developers, architects, data engineers, QA, and platform teams.\n\nProvide technical direction and review solution architecture, integrations, engineering practices, and production readiness.\n\nOversee initiatives involving Generative AI, enterprise assistants, agentic workflows, machine learning, and intelligent automation.\n\nManage project dependencies, resource capacity, budgets, risks, and delivery commitments across concurrent initiatives.\n\nEstablish AI governance covering data privacy, security, access controls, responsible AI, model evaluation, and human oversight.\n\nEnsure solutions meet agreed expectations for accuracy, reliability, performance, scalability, and operating cost.\n\nCollaborate with enterprise architecture, security, data, infrastructure, and operations teams to support implementation and adoption.\n\nPresent portfolio status, business outcomes, risks, and decisions requiring leadership attention through regular governance reviews.\n\nMentor technical leads, resolve delivery escalations, and promote reusable components and consistent engineering standards.\n\nRequired Experience and Skills\nSignificant experience in enterprise technology delivery, with demonstrated leadership of multiple projects and technical teams.\n\nProven experience delivering AI/ML or Generative AI solutions into production.\n\nStrong understanding of LLMs, retrieval-augmented generation (RAG), embeddings, vector databases, prompt engineering, agent orchestration, and evaluation frameworks.\n\nExperience with enterprise AI platforms such as OpenAI services, Azure AI, Google Cloud Vertex AI, or AWS Bedrock.\n\nStrong knowledge of APIs, microservices, enterprise integrations, cloud architecture, containers, and Kubernetes.\n\nFamiliarity with Python-based AI development, CI/CD, MLOps/LLMOps, observability, and model lifecycle management.\n\nAbility to assess architecture and technical trade-offs, challenge estimates, and guide teams through complex delivery issues.\n\nStrong stakeholder management, executive communication, negotiation, and team leadership skills.\n\nAbility to translate business needs into delivery plans and explain technical decisions clearly to nontechnical stakeholders.\n\nPreferred Qualifications\nExperience managing an enterprise AI portfolio or establishing an AI Centre of Excellence.\n\nExperience with enterprise knowledge integration, AI security guardrails, and organizational adoption.\n\nExposure to vendor management, commercial planning, and cloud cost optimization.\n\nBachelor’s or master’s degree in Computer Science, Engineering, or a related discipline.\n\nSuccess Measures\nPredictable delivery across the AI portfolio, measurable business value, stakeholder adoption, reliable production performance, and effective governance of AI risks and costs.\nKey Responsibilities\nJob Title: Senior Technical Manager - Enterprise AI\nRole Overview\nWe are seeking a Senior Technical Manager to lead the client’s Enterprise AI portfolio across multiple projects and teams. The role will own technical delivery, guide solution architecture, and work closely with business stakeholders and senior leadership to translate business priorities into secure, scalable AI solutions with measurable outcomes.\nKey Responsibilities\nManage the end-to-end AI project portfolio, including discovery, feasibility assessment, planning, development, deployment, and production support.\n\nPartner with business stakeholders to identify and prioritize AI use cases, define success criteria, and establish delivery roadmaps.\n\nLead cross-functional teams comprising AI/ML engineers, application developers, architects, data engineers, QA, and platform teams.\n\nProvide technical direction and review solution architecture, integrations, engineering practices, and production readiness.\n\nOversee initiatives involving Generative AI, enterprise assistants, agentic workflows, machine learning, and intelligent automation.\n\nManage project dependencies, resource capacity, budgets, risks, and delivery commitments across concurrent initiatives.\n\nEstablish AI governance covering data privacy, security, access controls, responsible AI, model evaluation, and human oversight.\n\nEnsure solutions meet agreed expectations for accuracy, reliability, performance, scalability, and operating cost.\n\nCollaborate with enterprise architecture, security, data, infrastructure, and operations teams to support implementation and adoption.\n\nPresent portfolio status, business outcomes, risks, and decisions requiring leadership attention through regular governance reviews.\n\nMentor technical leads, resolve delivery escalations, and promote reusable components and consistent engineering standards.\n\nRequired Experience and Skills\nSignificant experience in enterprise technology delivery, with demonstrated leadership of multiple projects and technical teams.\n\nProven experience delivering AI/ML or Generative AI solutions into production.\n\nStrong understanding of LLMs, retrieval-augmented generation (RAG), embeddings, vector databases, prompt engineering, agent orchestration, and evaluation frameworks.\n\nExperience with enterprise AI platforms such as OpenAI services, Azure AI, Google Cloud Vertex AI, or AWS Bedrock.\n\nStrong knowledge of APIs, microservices, enterprise integrations, cloud architecture, containers, and Kubernetes.\n\nFamiliarity with Python-based AI development, CI/CD, MLOps/LLMOps, observability, and model lifecycle management.\n\nAbility to assess architecture and technical trade-offs, challenge estimates, and guide teams through complex delivery issues.\n\nStrong stakeholder management, executive communication, negotiation, and team leadership skills.\n\nAbility to translate business needs into delivery plans and explain technical decisions clearly to nontechnical stakeholders.\n\nPreferred Qualifications\nExperience managing an enterprise AI portfolio or establishing an AI Centre of Excellence.\n\nExperience with enterprise knowledge integration, AI security guardrails, and organizational adoption.\n\nExposure to vendor management, commercial planning, and cloud cost optimization.\n\nBachelor’s or master’s degree in Computer Science, Engineering, or a related discipline.\n\nSuccess Measures\nPredictable delivery across the AI portfolio, measurable business value, stakeholder adoption, reliable production performance, and effective governance of AI risks and costs.\nSkill Requirements\nJob Title: Senior Technical Manager - Enterprise AI\nRole Overview\nWe are seeking a Senior Technical Manager to lead the client’s Enterprise AI portfolio across multiple projects and teams. The role will own technical delivery, guide solution architecture, and work closely with business stakeholders and senior leadership to translate business priorities into secure, scalable AI solutions with measurable outcomes.\nKey Responsibilities\nManage the end-to-end AI project portfolio, including discovery, feasibility assessment, planning, development, deployment, and production support.\n\nPartner with business stakeholders to identify and prioritize AI use cases, define success criteria, and establish delivery roadmaps.\n\nLead cross-functional teams comprising AI/ML engineers, application developers, architects, data engineers, QA, and platform teams.\n\nProvide technical direction and review solution architecture, integrations, engineering practices, and production readiness.\n\nOversee initiatives involving Generative AI, enterprise assistants, agentic workflows, machine learning, and intelligent automation.\n\nManage project dependencies, resource capacity, budgets, risks, and delivery commitments across concurrent initiatives.\n\nEstablish AI governance covering data privacy, security, access controls, responsible AI, model evaluation, and human oversight.\n\nEnsure solutions meet agreed expectations for accuracy, reliability, performance, scalability, and operating cost.\n\nCollaborate with enterprise architecture, security, data, infrastructure, and operations teams to support implementation and adoption.\n\nPresent portfolio status, business outcomes, risks, and decisions requiring leadership attention through regular governance reviews.\n\nMentor technical leads, resolve delivery escalations, and promote reusable components and consistent engineering standards.\n\nRequired Experience and Skills\nSignificant experience in enterprise technology delivery, with demonstrated leadership of multiple projects and technical teams.\n\nProven experience delivering AI/ML or Generative AI solutions into production.\n\nStrong understanding of LLMs, retrieval-augmented generation (RAG), embeddings, vector databases, prompt engineering, agent orchestration, and evaluation frameworks.\n\nExperience with enterprise AI platforms such as OpenAI services, Azure AI, Google Cloud Vertex AI, or AWS Bedrock.\n\nStrong knowledge of APIs, microservices, enterprise integrations, cloud architecture, containers, and Kubernetes.\n\nFamiliarity with Python-based AI development, CI/CD, MLOps/LLMOps, observability, and model lifecycle management.\n\nAbility to assess architecture and technical trade-offs, challenge estimates, and guide teams through complex delivery issues.\n\nStrong stakeholder management, executive communication, negotiation, and team leadership skills.\n\nAbility to translate business needs into delivery plans and explain technical decisions clearly to nontechnical stakeholders.\n\nPreferred Qualifications\nExperience managing an enterprise AI portfolio or establishing an AI Centre of Excellence.\n\nExperience with enterprise knowledge integration, AI security guardrails, and organizational adoption.\n\nExposure to vendor management, commercial planning, and cloud cost optimization.\n\nBachelor’s or master’s degree in Computer Science, Engineering, or a related discipline.\n\nSuccess Measures\nPredictable delivery across the AI portfolio, measurable business value, stakeholder adoption, reliable production performance, and effective governance of AI risks and costs.\nOther Requirements","description_format":"text","description_chars":10777,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Consulting & Digital Transformation","IT Outsourcing & Dedicated Teams","Systems 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