The Role
Lead the architecture, development, deployment, and operationalization of cloud-based AI/ML and Generative AI solutions. Design RAG pipelines, LLM applications, data strategies, MLOps workflows, and scalable cloud-native systems. Guide model deployment, monitoring, governance, and optimization while providing technical leadership to engineering and data science teams. Engage clients, define technical roadmaps, manage delivery, and coordinate stakeholders across AI/ML projects.
Summary Generated by Built In
Job Summary
We 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.
Key Responsibilities
Customer Engagement & Solution Architecture
- Interact with clients and stakeholders to gather business and technical requirements and translate them into scalable AI/ML solutions.
- Architect and design AI/ML systems across AWS, GCP, or Azure with a strong focus on cloud-native and cost-optimized architecture.
- Create detailed system design documents, architecture diagrams, and technical roadmaps.
- Define data architecture, storage, and retrieval strategies tailored to AI/ML workflows.
GenAI & RAG Architecture
- Lead the design and implementation of Generative AI solutions using LLMs, LangChain, LlamaIndex, Prompt Engineering, and vector databases such as Pinecone, FAISS, Weaviate, or Elasticsearch.
- Architect RAG (Retrieval-Augmented Generation) pipelines for enterprise use cases including knowledge management, chatbot development, and document summarization.
- Implement prompt orchestration, retrieval optimization, and grounding techniques to enhance LLM output accuracy and relevance.
AI/ML Model Development & MLOps
- Guide the development of Python-based APIs, data preprocessing workflows, and model training pipelines.
- Design and implement robust CI/CD pipelines for ML model deployment using tools like SageMaker, Vertex AI, or Azure ML.
- Define and implement model monitoring, retraining, and performance management strategies for production-grade ML systems.
- Ensure best practices in versioning, reproducibility, model lineage, and auditability (MLOps/LLMOps).
Technical Leadership & Governance
- Review and approve system designs, PoCs, and implementation approaches.
- Provide hands-on leadership and mentorship to data scientists, ML engineers, and software developers.
- Lead architectural decision-making, code quality reviews, and sprint grooming sessions.
- Champion best practices in security, compliance, scalability, and performance optimization for AI/ML solutions.
Project Management & Collaboration
- Own end-to-end technical delivery of AI/ML and GenAI projects across multiple domains (e.g., BFSI, Retail, Healthcare, Manufacturing).
- Coordinate with product owners, business analysts, data engineers, and DevOps teams to ensure seamless delivery.
- Manage stakeholder expectations, project timelines, and resource allocation efficiently.
Required Qualifications
- 7+ years of overall IT experience in designing, developing, deploying, and operationalizing AI/ML solutions.
- Minimum 3 years of experience in architecting end-to-end AI/ML solutions, including design, implementation, and production deployment.
- Proven experience in GenAI, LLMs, RAG architecture, prompt engineering, and orchestration tools like LangChain, LlamaIndex, etc.
- Hands-on with vector databases (e.g., Pinecone, FAISS, Elasticsearch) and unstructured data retrieval.
- Deep knowledge of Machine Learning and Deep Learning algorithms: CNNs, RNNs, LSTMs, Transformers, etc.
- Experience in Natural Language Processing (NLP), including language modeling, summarization, classification, and NER.
- Strong expertise in Python, with frameworks like PyTorch, TensorFlow, HuggingFace, NumPy, and Pandas.
- Demonstrated experience in designing cloud-native AI/ML solutions on AWS, GCP, or Azure.
- Skilled in deploying models via services like SageMaker, Vertex AI, Azure ML, or using containers and Kubernetes.
- Solid understanding of MLOps/LLMOps lifecycle: pipeline automation, model registry, monitoring, CI/CD.
- Excellent communication, leadership, and stakeholder management skills.
Preferred Qualifications
- Certification in AWS/GCP or ML specializations.
- Experience in leading large-scale AI transformation programs.
Why Join Us?
- Work with cutting-edge GenAI and AI/ML technologies and projects.
- Collaborate with top-tier clients and drive real-world impact.
- Leadership opportunities in a growing AI/ML practice.
Skills Required
- 7+ years of overall IT experience designing, developing, deploying, and operationalizing AI/ML solutions
- At least 3 years of experience architecting end-to-end AI/ML solutions through production deployment
- Experience with Generative AI, large language models, Retrieval-Augmented Generation, prompt engineering, and orchestration tools
- Hands-on experience with vector databases and unstructured data retrieval
- Deep knowledge of machine learning and deep learning algorithms, including CNNs, RNNs, LSTMs, and Transformers
- Experience with Natural Language Processing, including language modeling, summarization, classification, and named entity recognition
- Strong Python expertise and experience with PyTorch, TensorFlow, Hugging Face, NumPy, and Pandas
- Experience designing cloud-native AI/ML solutions on AWS, Google Cloud Platform, or Microsoft Azure
- Experience deploying models using SageMaker, Vertex AI, Azure Machine Learning, containers, or Kubernetes
- Understanding of MLOps and LLMOps, including pipeline automation, model registries, monitoring, and CI/CD
- Excellent communication, leadership, and stakeholder management skills
- AWS, Google Cloud, or machine learning certification
- Experience leading large-scale AI transformation programs
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The Company
What We Do
Relanto is a global advisory, consulting, and technology-services partner that helps organizations navigate business transformation as artificial intelligence reshapes the business landscape. Founded in 2021, the company combines advisory expertise with consulting and technology services, operating across a global footprint that includes India and the United States. Its mission centers on applying AI and modern technology to evolving enterprise needs.
