{"id":2780052,"url":"https://alion.io/job/relanto-aiml-architect","title":"AI/ML Architect","company":{"id":4442664,"name":"Relanto","domain":"relanto.ai","url":"https://alion.io/company/relanto-2","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Keka","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":31000,"max_usd":77000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":10},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"ElasticSearch","optional":false},{"name":"FAISS","optional":false},{"name":"GCP","optional":false},{"name":"Hugging Face","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"NER","optional":false},{"name":"NLP","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"TensorFlow","optional":false},{"name":"Transformers","optional":false},{"name":"Vertex AI","optional":false},{"name":"Weaviate","optional":false}],"status":"live","first_seen_at":"2026-05-18T04:01:58Z","employer_posted_date":"2026-05-18","last_verified_at":"2026-10-18T01:30:15Z","board_verified":true,"closed_at":null,"days_open":152,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":152},"description":"Job Summary\nWe are seeking an experienced and visionary AI/ML Architect to lead the end-to-end design, development, deployment, and operationalization of advanced AI/ML and Generative AI (GenAI) solutions on cloud platforms. The ideal candidate will possess deep technical expertise in ML architecture, GenAI frameworks, Retrieval-Augmented Generation (RAG) pipelines, cloud-native deployment, and MLOps practices. You will work closely with cross-functional teams, clients, and engineering teams to define scalable AI strategies and deliver cutting-edge solutions across various domains.\nKey Responsibilities\nCustomer Engagement & Solution Architecture\nInteract with clients and stakeholders to gather business and technical requirements and translate them into scalable AI/ML solutions.\nArchitect and design AI/ML systems across AWS, GCP, or Azure with a strong focus on cloud-native and cost-optimized architecture.\nCreate detailed system design documents, architecture diagrams, and technical roadmaps.\nDefine data architecture, storage, and retrieval strategies tailored to AI/ML workflows.\nGenAI & RAG Architecture\nLead the design and implementation of Generative AI solutions using LLMs, LangChain, LlamaIndex, Prompt Engineering, and vector databases such as Pinecone, FAISS, Weaviate, or Elasticsearch.\nArchitect RAG (Retrieval-Augmented Generation) pipelines for enterprise use cases including knowledge management, chatbot development, and document summarization.\nImplement prompt orchestration, retrieval optimization, and grounding techniques to enhance LLM output accuracy and relevance.\nAI/ML Model Development & MLOps\nGuide the development of Python-based APIs, data preprocessing workflows, and model training pipelines.\nDesign and implement robust CI/CD pipelines for ML model deployment using tools like SageMaker, Vertex AI, or Azure ML.\nDefine and implement model monitoring, retraining, and performance management strategies for production-grade ML systems.\nEnsure best practices in versioning, reproducibility, model lineage, and auditability (MLOps/LLMOps).\nTechnical Leadership & Governance\nReview and approve system designs, PoCs, and implementation approaches.\nProvide hands-on leadership and mentorship to data scientists, ML engineers, and software developers.\nLead architectural decision-making, code quality reviews, and sprint grooming sessions.\nChampion best practices in security, compliance, scalability, and performance optimization for AI/ML solutions.\nProject Management & Collaboration\nOwn end-to-end technical delivery of AI/ML and GenAI projects across multiple domains (e.g., BFSI, Retail, Healthcare, Manufacturing).\nCoordinate with product owners, business analysts, data engineers, and DevOps teams to ensure seamless delivery.\nManage stakeholder expectations, project timelines, and resource allocation efficiently.\nRequired Qualifications\n7+ years of overall IT experience in designing, developing, deploying, and operationalizing AI/ML solutions.\nMinimum 3 years of experience in architecting end-to-end AI/ML solutions, including design, implementation, and production deployment.\nProven experience in GenAI, LLMs, RAG architecture, prompt engineering, and orchestration tools like LangChain, LlamaIndex, etc.\nHands-on with vector databases (e.g., Pinecone, FAISS, Elasticsearch) and unstructured data retrieval.\nDeep knowledge of Machine Learning and Deep Learning algorithms: CNNs, RNNs, LSTMs, Transformers, etc.\nExperience in Natural Language Processing (NLP), including language modeling, summarization, classification, and NER.\nStrong expertise in Python, with frameworks like PyTorch, TensorFlow, HuggingFace, NumPy, and Pandas.\nDemonstrated experience in designing cloud-native AI/ML solutions on AWS, GCP, or Azure.\nSkilled in deploying models via services like SageMaker, Vertex AI, Azure ML, or using containers and Kubernetes.\nSolid understanding of MLOps/LLMOps lifecycle: pipeline automation, model registry, monitoring, CI/CD.\nExcellent communication, leadership, and stakeholder management skills.\nPreferred Qualifications\nCertification in AWS/GCP or ML specializations.\nExperience in leading large-scale AI transformation programs.\nWhy Join Us?\nWork with cutting-edge GenAI and AI/ML technologies and projects.\nCollaborate with top-tier clients and drive real-world impact.\nLeadership opportunities in a growing AI/ML practice.","description_format":"text","description_chars":4393,"description_truncated":false,"requirements":{"experience_years_min":7,"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":[],"lifecycle":[{"event":"open","at":"2026-10-17T12:17:51Z"}],"visa":[],"liveness":{"score":11,"band":"cold","label":"Long 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