{"id":2526339,"url":"https://alion.io/job/tkxel-ai-lead-ai-architect","title":"AI Lead / AI Architect","company":{"id":62299,"name":"Tkxel","domain":"tkxel.com","url":"https://alion.io/company/tkxel","size_band":null,"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":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Lahore, Pakistan"],"countries":["PK"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":25000,"max_usd":61000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1199},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"Hallucination","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"RAG","optional":false}],"status":"live","first_seen_at":"2026-10-14T12:23:37Z","employer_posted_date":"2026-10-14","last_verified_at":"2026-10-26T04:59:15Z","board_verified":true,"closed_at":null,"days_open":11,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":11},"description":"AI Lead / AI Architect\nAI Lead / AI Architect with a minimum of 7 years of experience in AI/ML, data science, software engineering, or related fields, including strong hands-on experience designing and delivering enterprise AI/ML solutions. Will provide technical leadership for AI initiatives, define AI architecture and solution patterns, and guide AI/ML solutions from business requirements through production deployment.\nKey Responsibilities\nDefine the AI/ML architecture and technical strategy for enterprise AI initiatives.\n\nTranslate business objectives and use cases into scalable, secure, and production-ready AI solutions.\n\nLead the design of end-to-end AI architectures, including models, data, APIs, applications, infrastructure, and integration components.\n\nEvaluate AI/ML technologies, frameworks, platforms, and models and recommend the most appropriate solutions.\n\nEstablish reusable AI architecture patterns, standards, and best practices.\n\nDefine technical approaches for traditional ML, Generative AI, LLMs, RAG, and AI Agents.\n\nWork closely with Business Analysts and Product teams to assess AI use cases, technical feasibility, value, data requirements, and dependencies.\n\nGuide Data Scientists and AI/ML Engineers in model development, experimentation, evaluation, and productionization.\n\nWork with Data Engineers to define data architecture and pipelines required for AI/ML solutions.\n\nWork with Backend and Frontend teams to integrate AI capabilities into enterprise applications.\n\nWork with DevOps/MLOps teams to establish production deployment architectures across on-premises Kubernetes and cloud environments.\n\nDefine requirements for model serving, inference, scalability, monitoring, and lifecycle management.\n\nEstablish appropriate approaches for model versioning, deployment, monitoring, retraining, and rollback.\n\nEnsure AI solutions meet requirements for security, performance, scalability, availability, and maintainability.\n\nIdentify technical risks, architectural constraints, dependencies, and technology gaps.\n\nLead technical design reviews and provide guidance on complex AI/ML architecture decisions.\n\nEnsure AI solutions are aligned with enterprise architecture and security standards.\n\nProvide technical leadership and mentorship to AI/ML and engineering teams.\n\nStay current with developments in Generative AI, LLMs, AI Agents, MLOps, and AI engineering and assess their relevance to the organization.\n\nGenerative AI G LLM\nDesign architectures for LLM-powered applications and enterprise Generative AI solutions.\n\nDefine appropriate approaches for:\n\nRAG\n\nEmbeddings\n\nVector databases\n\nPrompt engineering\n\nLLM orchestration\n\nAI Agents\n\nTool/function calling\n\nModel routing and selection\n\nEvaluate commercial and open-source models based on performance, cost, latency, security, and business requirements.\n\nDesign solutions that support multiple LLM providers or models where appropriate.\n\nEstablish approaches for LLM evaluation, monitoring, and quality measurement.\n\nAddress AI-specific concerns such as hallucination, data leakage, prompt injection, access control, and model reliability.\n\nGuide teams in selecting appropriate frameworks such as LangGraph, LangChain, or similar AI application frameworks.\n\nAI/ML Architecture\nDesign end-to-end architectures covering:\n\nData → ML/AI Model → Model Serving → API → Application → Monitoring\nDefine appropriate model development and production environments.\n\nEstablish patterns for real-time and batch inference.\n\nDefine integration patterns for AI/ML models with enterprise applications and APIs.\n\nEnsure AI solutions can operate across on-premises, cloud, and hybrid environments.\n\nDesign for scalability and high availability of AI/ML workloads.\n\nDefine appropriate compute requirements, including CPU/GPU resources where applicable.\n\nEstablish architecture patterns for AI/ML observability and monitoring.\n\nMLOps G Productionization\nDefine the MLOps architecture and lifecycle for AI/ML solutions.\n\nWork with DevOps/MLOps teams to implement automated ML/AI deployment pipelines.\n\nEstablish standards for:\n\nModel versioning\n\nModel registry\n\nExperiment tracking\n\nModel deployment\n\nModel monitoring\n\nModel rollback\n\nModel retraining\n\nGuide the deployment of models to Kubernetes-based environments.\n\nEnsure AI/ML solutions can be deployed consistently across development, testing, and production.\n\nDefine requirements for model performance monitoring and detection of data/model drift.\n\nSupport the transition of prototypes and proof-of-concepts into production-grade solutions.\n\nAI Governance G Responsible AI\nDefine technical controls for AI security, privacy, governance, and responsible AI.\n\nEstablish appropriate human oversight for AI-powered 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