AI / ML Architect

Job Title : AI / ML Architect

Location : Bangalore / Pune

Experience : 10+ Years

Job Type : Full-Time | Permanent

Work Mode : Hybrid

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

10+ 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.

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.