{"id":1903603,"url":"https://alion.io/job/dentsu-ai-lead-architect","title":"AI Lead Architect","company":{"id":888,"name":"dentsu","domain":"dentsu.com","url":"https://alion.io/company/dentsu-polska","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":80,"open_postings":139,"ghost_share":0.007,"stale_share":0.784,"repost_share":0.022,"time_to_fill_p50_days":29,"computed_at":"2026-10-29T05:49:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":18500,"max_usd":49000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":47},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A2A","optional":false},{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Django","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Flask","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Model Distillation","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"QLoRA","optional":false},{"name":"Quantization","optional":false},{"name":"RAG","optional":false},{"name":"RLAIF","optional":false},{"name":"RLHF","optional":false},{"name":"SFT","optional":false},{"name":"SQL","optional":false},{"name":"Transformers","optional":false},{"name":"LangChain","optional":true},{"name":"PEFT","optional":true}],"status":"live","first_seen_at":"2026-10-05T12:03:18Z","employer_posted_date":"2026-10-05","last_verified_at":"2026-10-30T01:04:10Z","board_verified":true,"closed_at":null,"days_open":24,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":24},"description":"Job Description:\nKey Responsibilities\n\nSolution Engineering & Technical Execution:\no Lead the hands-on engineering for end-to-end AI solutions across Deep Learning,\nGenAI, Agentic AI, and multimodal use cases.\no Apply rigorous \"fail fast\" logic to all AI project management. Quickly identify,\nevaluate, and disqualify unviable AI use cases based on technical feasibility, effort,\ncost, and risk early in the cycle.\no Perform explicit trade-off analysis on model class (frontier vs. SLM vs. fine-tuned),\nretrieval design, memory optimization, and orchestration.\no Lead solutioning, support architecture for end-to-end AI solutions across GenAI,\nAgentic AI, multimodal, and applied ML use cases, with explicit trade-off analysis\non model class (frontier vs. SLM vs. fine-tuned), retrieval design, memory, and\norchestration.\no Own the practice's reference architectures and solution design patterns for\nmultimodal agentic systems, including planning, tool use, memory, grounding, and\ninter-agent communication (MCP, A2A).\no Conduct solution design reviews across concurrent client engagements; facilitate\nsubjective technical decisions and enable delivery excellence.\nMultimodal Agentic Systems & SLM Design:\no Design and lead the build of multi-agent systems with reasoning, planning, tool\nuse, persistent memory, and grounded retrieval.\no Lead multimodal system design and solutions across text, vision, speech, and\nstructured data, including ingestion, representation, and downstream agent\nreasoning.\no Establish patterns for SLM design and adoption - distillation, fine-tuning,\nquantization, and routing - to meet enterprise constraints on cost, latency, data\nresidency, and on-prem/edge deployment\no Define hybrid retrieval and knowledge architectures spanning vector, graph (KG),\nand NoSQL stores; lead KG-assisted retrieval, entity linking, and structured\ngrounding.\n\nEval, Guardrails & Production Quality:\no Establish evaluation as a first-class discipline: design eval frameworks, golden\ndatasets, regression suites, automated and human-in-the-loop evals, and\nobservability for agentic and generative systems.\no Define and enforce safety, guardrail, and hallucination-control standards across\nthe practice; lead red-teaming and adversarial testing for high-stakes\ndeployments.\no Set the bar for production readiness-reliability, latency, cost, monitoring, drift\ndetection, and incident response-for AI systems in regulated, enterprise-grade\nenvironments.\no Lead GPU/accelerator ops, model serving, and lifecycle automation for\ndeployment across cloud hyper-scalers, on-prem, and edge.\n\nTechnical Leadership & Capability Pillars:\no Act as a technical sentinel for the AI practice, mentoring engineers through\nrigorous code and architecture reviews to ensure permanent capability building\nrather than temporary crisis management.\no Establish and enforce AI in SDLC frameworks on delivery projects.\n\nCross-functional Leadership & Delivery\no Engage with client and stakeholder leadership on architecture, feasibility, and risk;\ncommunicate technical direction clearly to non-technical audiences.\no Support pre-sales and solutioning for new GenAI and Agentic AI opportunities,\nincluding effort estimation, architectural framing, and capability storytelling.\nMust Have\nTechnical Skills\nDeep Learning & Machine Learning: Strong hands-on experience with neural networks, Transformers, predictive modeling, embeddings, and vector search.\nGenerative AI: Hands-on experience with LLMs/SLMs, RAG/Agentic RAG, agents, prompt engineering, grounding, multimodal architectures, and production GenAI solutions.\nFine-tuning: Practical experience with techniques such as SFT, LoRA/QLoRA, RLHF/RLAIF, distillation, and/or quantization.\nAgentic AI: Hands-on experience with multi-agent orchestration, planning, tool use, memory, and agentic workflows. Experience with frameworks such as LangGraph, LlamaIndex, or AutoGen.\nProgramming & Engineering: Advanced Python, SQL, strong API/backend engineering experience using FastAPI, Flask, Django, or equivalent frameworks.\nProduction Engineering: Proven experience designing, developing, testing, and deploying AI/ML solutions in enterprise production environments.\nCloud: Strong hands-on experience with at least one major cloud platform - AWS, Azure, or GCP.\nData/Storage: Experience with databases and data platforms such as MongoDB, NoSQL, vector databases, graph databases, or equivalent.\nExperience: Minimum 8 years of total hands-on software development/engineering experience.\nAI Experience: Minimum 3+ years of hands-on experience building and deploying Deep Learning/AI systems in production.\nGenAI/Agentic AI: Demonstrable hands-on experience beyond basic API integrations or simple RAG implementations, such as multi-agent systems, custom fine-tuning, advanced RAG, or SLM deployments.\nWork Location: Willingness to work from the Pune office at least 3 days per week.\nGood to have:\nExperience with commerce cloud ecosystems (Salesforce and\nAdobe).\nAttitude & Mindset\nEquipped with a builder's hands and a highly pragmatic approach to enterprise AI.\nPrioritizes technical validation, pragmatic domain expertise, and rigorous testing over\n\"AI hype.\"\nOpen and flexible toward a hybrid work structure with no less than 3 days work from\nthe office in Pune, ensuring regular connection and cross-project knowledge.\nLocation:\nPuneBrand:\nMerkleTime Type:\nFull timeContract Type:\nPermanent","description_format":"text","description_chars":5423,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Hybrid 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