AI/ML Architect

Indihire Consultants; Bengaluru; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Indihire Consultants in Bengaluru is seeking a seasoned AI/ML Architect to design scalable GenAI/LLM solutions across cloud platforms. You will lead architecture, data strategy, and RAG pipelines, working with Python teams to deploy production systems.

You will mentor data scientists and software engineers, drive governance, and optimize costs. This role emphasizes cloud‑native, scalable AI/ML workflows and collaboration with cross‑functional stakeholders.

Qualifications

• 7+ years of overall IT experience focused on AI/ML systems.
• 3+ years architecting end‑to‑end AI/ML solutions and deployment.
• Hands‑on with GenAI, LLMs, RAG architecture, and orchestration tools.
• Experience with vector databases and unstructured data retrieval.
• Strong Python and ML stack knowledge; cloud‑native design.

Responsibilities

• Interact with clients to gather business/tech requirements for AI/ML solutions.
• Architect and design AI/ML systems across AWS, GCP, Azure with cost optimization.
• Lead GenAI & RAG architecture, prompt engineering, and vector DB usage.
• Guide ML model development, CI/CD pipelines, and MLOps practices.
• Provide technical leadership, governance, and code reviews.
• Own end‑to‑end delivery across multiple domains.

Skills

AI/ML architecture

GenAI & LLMs

MLOps/LLMOps

Python

Cloud platforms

NLP

Team leadership

Communication

Tools

Pinecone

FAISS

Weaviate

Elasticsearch

LangChain

LlamaIndex

SageMaker

Vertex AI

Azure ML

Docker

Kubernetes

Job description

Role & 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.

Preferred candidate profile

• 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.