Cloud Architect

HTC Global Services; Chennai; On-site; INR 4,000,000 - 7,500,000; Full time

Job summary

HTC Global Services is seeking an AWS AI Architect to define and own enterprise AI/GenAI architecture, from data foundations to deployment, governance, and monitoring, collaborating with business, data, and risk teams.

You will lead design of cloud-native apps with AWS services, mentor engineers, and drive scalable, secure, and compliant production systems. Excellent communication and strong MLOps, security, and cost-control expertise are essential.

Qualifications

• 10+ years in software or data engineering with 4+ years architecting AI/ML solutions in production.

Responsibilities

• Design end-to-end AI/ML and GenAI architectures (RAG, agents, fine-tuning, model serving).
• Evaluate and select LLMs, vector databases, orchestration frameworks, and MLOps tooling.
• Define reference architectures, patterns, and standards for reuse across teams.
• Partner with data engineering on pipelines, feature stores, and data quality for AI workloads.
• Build in responsible AI: model risk management, explainability, bias testing, audit trails, and regulatory compliance.
• Establish MLOps/LLMOps practices covering CI/CD, evaluation, observability, drift detection, and cost control.
• Lead proofs of concept and pilots, and move them to production.
• Advise stakeholders and executives on AI strategy, feasibility, and trade-offs.
• Mentor engineers and data scientists, and run architecture reviews.

Skills

LLM architectures

Python

AWS cloud

MLOps

Data engineering

Risk & compliance

Communication

Education

Bachelor's in CS/related field

Master's degree (preferred)

Tools

LangChain

PyTorch

TensorFlow

SageMaker

Kubernetes

Docker

OpenSearch

Job description

The AWS AI Architect defines and owns the technical architecture for enterprise AI and GenAI solutions, from data foundations and model selection through deployment, governance, and monitoring. You work with business, data, engineering, and risk teams to turn use cases into scalable, secure, compliant production systems. Lead the design and development of our cloud-native applications. In this role, you will bridge the gap between business needs and technical execution, turning complex requirements into resilient, secure, and cost-effective distributed systems. You will work closely with software developers, cloud engineers, and product owners to build high-performance applications leveraging modern AWS services.

Key responsibilities:

• Design end-to-end AI/ML and GenAI architectures (RAG, agents, fine-tuning, model serving) that integrate with existing enterprise platforms.
• Evaluate and select LLMs, vector databases, orchestration frameworks, and MLOps tooling.
• Define reference architectures, patterns, and standards for reuse across teams.
• Partner with data engineering on pipelines, feature stores, and data quality for AI workloads.
• Build in responsible AI: model risk management, explainability, bias testing, audit trails, and regulatory compliance.
• Establish MLOps/LLMOps practices covering CI/CD, evaluation, observability, drift detection, and cost control.
• Lead proofs of concept and pilots, and move them to production.
• Advise stakeholders and executives on AI strategy, feasibility, and trade-offs.
• Mentor engineers and data scientists, and run architecture reviews.

Required qualifications:

• 10+ years in software or data engineering, with 4+ years architecting AI/ML solutions in production.
• Hands-on experience with LLMs and GenAI: prompt engineering, RAG, embeddings, agent frameworks (e.g., LangChain, LlamaIndex).
• Strong Python skills and fluency in ML frameworks (PyTorch, TensorFlow, scikit-learn).
• Deep experience with at least AWS cloud sand its AI services (e.g., SageMaker, Bedrock, Azure OpenAI, Vertex AI).
• Application Design: Design scalable, fault-tolerant, and high-availability cloud applications using core AWS services like Lambda, API Gateway, ECS/EKS, and DynamoD
• Experience with MLOps tooling (MLflow, Kubeflow, Docker, Kubernetes, CI/CD).
• Solid grounding in data architecture: data lakes/warehouses, streaming, APIs, and microservices.
• Knowledge of security, privacy, and governance for AI systems.
• Excellent communication, with the ability to explain technical trade-offs to non-technical leaders.

Preferred qualifications:

• Experience in banking, insurance, or asset/wealth management, including regulatory expectations (e.g., SR 11-7 model risk).
• Experience with vector databases (Pinecone, pgvector, OpenSearch) and fine-tuning or evaluation frameworks.
• Cloud or architecture certifications (e.g., AWS ML Specialty, AWS AI Engineer, TOGAF).
• Master's or PhD in Computer Science, Data Science, or a related field.