What are the responsibilities and job description for the AI/Ml Architect position at Relanto?
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, with minimum of 5 years in designing, developing, deploying, and operationalizing AI/ML solutions.
- Minimum 2–3 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.