{"id":1452470,"url":"https://alion.io/job/antino-ai-solution-architect","title":"AI Solution Architect","company":{"id":9454,"name":"Antino","domain":"antino.com","url":"https://alion.io/company/antino","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Solutions","role_family":"Solutions","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Gurgaon, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":29000,"max_usd":52000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":28},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A2A","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"Arize Phoenix","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"Chroma","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Context Engineering","optional":false},{"name":"CrewAI","optional":false},{"name":"DeepEval","optional":false},{"name":"DeepSeek","optional":false},{"name":"Docker","optional":false},{"name":"ElasticSearch","optional":false},{"name":"Embeddings","optional":false},{"name":"EU AI Act","optional":false},{"name":"FAISS","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Google ADK","optional":false},{"name":"GraphRAG","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"Langfuse","optional":false},{"name":"LangGraph","optional":false},{"name":"LangSmith","optional":false},{"name":"Llama","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"LoRA","optional":false},{"name":"Milvus","optional":false},{"name":"Mistral","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":"NIST AI RMF","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenAI Agents SDK","optional":false},{"name":"OpenSearch","optional":false},{"name":"OWASP Top 10","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Caching","optional":false},{"name":"Python","optional":false},{"name":"Qdrant","optional":false},{"name":"QLoRA","optional":false},{"name":"Qwen","optional":false},{"name":"RAG","optional":false},{"name":"Ragas","optional":false},{"name":"TGI","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"Tool Use","optional":false},{"name":"vLLM","optional":false},{"name":"Weaviate","optional":false},{"name":"BigQuery","optional":true},{"name":"Computer Vision","optional":true},{"name":"Databricks","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Machine Learning","optional":true},{"name":"PEFT","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Snowflake","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-09-29T06:50:59Z","employer_posted_date":null,"last_verified_at":"2026-09-29T06:50:59Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"About the Role : \n\nWe are looking for a hands-on AI Solution Architect who can act as the technical face of Antino's AI practice with enterprise clients and also build what is proposed.\n\nYou will lead technical discovery and pre-sales discussions, understand business challenges, design AI/GenAI/Agentic AI solutions, estimate effort, explain trade-offs and risks, and work closely with clients, sales, engineering, and data science teams.\n\nThis is not a slides-only role. You will build POCs, run demos, write code, define architecture, review production systems, and guide solutions from POC to production.\n\nKey Responsibilities : \n\n1. Pre-Sales & Client Solutioning : \n\n- Lead technical discovery calls, workshops, RFP/RFI responses, proposals, SOWs, estimates, and ROI discussions.\n\n- Translate business problems into practical AI/ML, GenAI, and Agentic AI solutions.\n\n- Present solutions and technical trade-offs to CXOs, architects, security, data, and engineering teams.\n\n- Build rapid POCs and demos to validate and de-risk solutions.\n\n2. Solution Architecture & Delivery : \n\n- Design scalable AI architectures using LLMs, RAG, AI Agents, vector databases, embeddings, prompt/context engineering, and knowledge graphs.\n\n- Select the right approach across classical ML, LLMs, fine-tuning, RAG, agents, or hybrid solutions.\n\n- Build and deploy solutions across AWS, Azure, or GCP.\n\n- Design APIs, data pipelines, model serving, enterprise integrations, and microservices.\n\n- Build production-grade agentic systems including tool calling, multi-agent workflows, memory, human-in-the-loop, and agent hand-offs.\n\n- Establish best practices for security, scalability, performance, observability, evaluation, reliability, and cost optimisation.\n\n3. AI Engineering & Team Enablement : \n\n- Stay hands-on with Python, FastAPI, AI frameworks, LLM platforms, and cloud technologies.\n\n- Guide engineering and data science teams from POC through production.\n\n- Implement AI evaluation, monitoring, guardrails, and LLMOps practices.\n\n- Train internal teams and create reusable reference architectures, accelerators, and playbooks.\n\n- Track emerging AI models, frameworks, protocols, and research and apply relevant innovations to Antino's AI practice.\n\nMust-Have Skills & Experience : \n\n- 5+ years in software engineering, data science, ML engineering, or solution architecture.\n\n- 2+ years of hands-on experience designing and deploying GenAI/LLM solutions in production.\n\n- Strong experience building Agentic AI, including tool-calling agents, multi-agent systems, agentic RAG, memory, and orchestration.\n\n- Strong foundations in statistics, classical ML, deep learning, feature engineering, model evaluation, and time series.\n\n- Strong hands-on Python and API development, preferably FastAPI.\n\n- Experience with LLM platforms such as OpenAI, Azure OpenAI, Anthropic Claude, Gemini, AWS Bedrock, and open-weight models such as Llama, Qwen, Mistral, or DeepSeek.\n\n- Experience with frameworks such as LangGraph, LangChain, LlamaIndex, CrewAI, OpenAI Agents SDK, Claude Agent SDK, or Google ADK.\n\n- Experience with vector/search technologies such as Pinecone, Weaviate, Milvus, Qdrant, pgvector, FAISS, Chroma, Elasticsearch/OpenSearch.\n\n- Strong knowledge of Advanced RAG, GraphRAG, hybrid search, re-ranking, query rewriting, multimodal RAG, and context engineering.\n\n- Experience with MLOps/LLMOps, Docker, Kubernetes, CI/CD, model serving, monitoring, evaluation, and cost optimisation.\n\n- Strong understanding of distributed systems, microservices, databases, APIs, and secure enterprise architecture.\n\n- Strong client-facing and communication skills with the ability to explain complex technical concepts to both CXOs and engineering teams.\n\nAdvanced AI Expertise : \n\n- Agent Design : Tool/function calling, ReAct, planning, reflection, memory, state management, HITL, failure recovery.\n\n- Multi-Agent Systems : Supervisor, hierarchical and peer-to-peer patterns, task routing and agent hand-offs.\n\n- Agent Protocols : MCP and A2A.\n\n- RAG : Hybrid search, re-ranking, GraphRAG, knowledge graphs, agentic/multimodal RAG, permission-aware retrieval.\n\n- Model Adaptation : Reasoning, long-context and multimodal models, SLMs, LoRA/QLoRA, distillation, prompting vs RAG vs fine-tuning.\n\n- Inference : vLLM/TGI, quantisation, prompt caching, model routing, latency and token-cost optimisation.\n\n- Evaluation : Ragas, DeepEval, LangSmith, Langfuse, Arize Phoenix, golden datasets, LLM-as-a-Judge, tracing.\n\n- AI Security & Governance : Prompt injection defence, guardrails, PII protection, OWASP Top 10 for LLMs, NIST AI RMF, ISO/IEC 42001, EU AI Act, and India's DPDP Act.\n\nGood to Have : \n\n- Experience in IT services/consulting with global clients across the US, UK, or Middle East.\n\n- Experience with enterprise knowledge platforms, knowledge graphs, or Company Brain-style systems.\n\n- Production experience in Voice AI, Document AI, or Computer Vision.\n\n- Experience with Databricks, Snowflake, BigQuery, or Spark.\n\n- Domain exposure to BFSI, Healthcare, Retail, Logistics, or Manufacturing.\n\n- AWS, Azure, or Google Cloud certifications.\n\n- Open-source contributions, research papers, patents, technical blogs, or conference talks.\n\nAbout Antino : \n\nAntino is an AI-native technology consulting company helping organisations embed intelligence into the way they operate.\n\n600+ Engineers | 50 AI Specialists | 400 Projects Delivered | 20 Countries Served\n\nWith offices across India, the US, UK, and UAE, Antino has developed Company Brain - a governed intelligence layer connecting enterprise knowledge, people, systems, and workflows to enable smarter decisions and coordinated action.\nSkills\nPython, FastAPI, LangChain, LangGraph, Machine Learning, Generative AI, ElasticSearch, Kubernetes, Docker","description_format":"text","description_chars":5797,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"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 Transformation","IT Outsourcing & Dedicated Teams","Custom Software Development","AI Consulting & Integration"],"lifecycle":[{"event":"open","at":"2026-09-29T08:00:48Z"}],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":1,"expected_fill_days":30,"reasons":["seen:1","velocity","win:early"],"computed_at":"2026-10-01T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/antino-ai-solution-architect","json_url":"https://alion.io/job/antino-ai-solution-architect.json","meta":{"generated_at":"2026-10-01T19:06:19Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":1555,"day_limit":5000,"remaining_today":3445,"minute_limit":60,"resets_at":"2026-10-02T00:00:00Z"}}}