Machine Learning Architect
Location not identified
- Pay
- Salary not listed in the saved posting
- Work setup
Remote stated — work setup source
This is a remote position. Job Summary
Read the full posting- Employment
- Unconfirmed
What you’ll work on
Full postingDesign and implement scalable, secure, and high-performance architectures on AWS for AI/ML applications.
Design Conversational AI, RAG (Retrieval-Augmented Generation), and Generative AI architectures using models like Claude (Anthropic), Mistral, Llama, and Titan.
Ensure high availability, disaster recovery, and cost optimization in cloud deployments.
From the employer’s posting
Technical Leadership & Architecture Design and implement scalable, secure, and high-performance architectures on AWS for AI/ML applications. Architect multi-tenant, enterprise-grade AI/ML solutions using AWS services like SageMaker, Bedrock, Lambda, API Gateway, DynamoDB, ECS, S3, OpenSearch, and Step Functions.
AI/ML & Generative AI Expertise Design Conversational AI, RAG (Retrieval-Augmented Generation), and Generative AI architectures using models like Claude (Anthropic), Mistral, Llama, and Titan. Optimize LLM inference pipelines, embeddings, vector search, and hybrid retrieval strategies for AI-based applications.
Architect event-driven, serverless, and microservices architectures for AI/ML applications. Ensure high availability, disaster recovery, and cost optimization in cloud deployments. Implement IAM, VPC, security best practices, and compliance.
Tools in this posting
- Python
- AWS
- Dynamodb
- S3
- SageMaker
- Terraform
- PyTorch
- TensorFlow
- Huggingface
Source — Tool mentions in context
Team & Client Engagement Lead and mentor a team of ML engineers, Python Developer and Cloud Engineers. Collaborate with business stakeholders, product teams, and multiple clients to define requirements and deliver AI/ML/GenAI-driven solutions.
Job Summary We are looking for a highly skilled Technical Architect with expertise in AWS, Generative AI, AI/ML, and scalable production-level architectures. The ideal candidate should have experience handling multiple clients, leading technical teams, and designing end-to-end cloud-based AI solutions with an overall experience of 9-12 years. This role involves architecting AI/ML/GenAI-driven applications, ensuring best practices in cloud deployment, security, and scalability while collaborating with cross-functional teams.
Technical Leadership & Architecture Design and implement scalable, secure, and high-performance architectures on AWS for AI/ML applications. Architect multi-tenant, enterprise-grade AI/ML solutions using AWS services like SageMaker, Bedrock, Lambda, API Gateway, DynamoDB, ECS, S3, OpenSearch, and Step Functions.
Design and implement scalable, secure, and high-performance architectures on AWS for AI/ML applications. Architect multi-tenant, enterprise-grade AI/ML solutions using AWS services like SageMaker, Bedrock, Lambda, API Gateway, DynamoDB, ECS, S3, OpenSearch, and Step Functions. Lead full lifecycle development of AI/ML/GenAI solutions—from PoC to production—ensuring reliability and performance.
Lead full lifecycle development of AI/ML/GenAI solutions—from PoC to production—ensuring reliability and performance. Define and implement best practices for MLOps, DataOps, and DevOps on AWS. AI/ML & Generative AI Expertise
Optimize LLM inference pipelines, embeddings, vector search, and hybrid retrieval strategies for AI-based applications. Drive ML model training, deployment, and monitoring using AWS SageMaker and AI/ML pipelines. Cloud & Infrastructure Management
Cloud & DevOps AWS Services: Bedrock, SageMaker, Lambda, API Gateway, DynamoDB, S3, ECS, Fargate, OpenSearch, RDS MLOps: SageMaker Pipelines, CI/CD (CodePipeline, GitHub Actions, Terraform, CDK)
AWS Services: Bedrock, SageMaker, Lambda, API Gateway, DynamoDB, S3, ECS, Fargate, OpenSearch, RDS MLOps: SageMaker Pipelines, CI/CD (CodePipeline, GitHub Actions, Terraform, CDK) Security: IAM, VPC, CloudTrail, GuardDuty, KMS, Cognito
LLMs & Generative AI: Bedrock (Claude, Mistral, Titan), OpenAI, Llama ML Frameworks: TensorFlow, PyTorch, LangChain, Hugging Face Vector DBs: OpenSearch, Pinecone, FAISS
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Jun 2, 2026 · 136d ago
- Recorded sightings
- 51
- Last seen by us
- Oct 15, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
Job description
This is a remote position.
Job Summary
We are looking for a highly skilled Technical Architect with expertise in AWS, Generative AI, AI/ML, and scalable production-level architectures. The ideal candidate should have experience handling multiple clients, leading technical teams, and designing end-to-end cloud-based AI solutions with an overall experience of 9-12 years.
This role involves architecting AI/ML/GenAI-driven applications, ensuring best practices in cloud deployment, security, and scalability while collaborating with cross-functional teams.
Key Responsibilities
Technical Leadership & Architecture
Design and implement scalable, secure, and high-performance architectures on AWS for AI/ML applications.
Architect multi-tenant, enterprise-grade AI/ML solutions using AWS services like SageMaker, Bedrock, Lambda, API Gateway, DynamoDB, ECS, S3, OpenSearch, and Step Functions.
Lead full lifecycle development of AI/ML/GenAI solutions—from PoC to production—ensuring reliability and performance.
Define and implement best practices for MLOps, DataOps, and DevOps on AWS.
AI/ML & Generative AI Expertise
Design Conversational AI, RAG (Retrieval-Augmented Generation), and Generative AI architectures using models like Claude (Anthropic), Mistral, Llama, and Titan.
Optimize LLM inference pipelines, embeddings, vector search, and hybrid retrieval strategies for AI-based applications.
Drive ML model training, deployment, and monitoring using AWS SageMaker and AI/ML pipelines.
Cloud & Infrastructure Management
Architect event-driven, serverless, and microservices architectures for AI/ML applications.
Ensure high availability, disaster recovery, and cost optimization in cloud deployments.
Implement IAM, VPC, security best practices, and compliance.
Team & Client Engagement
Lead and mentor a team of ML engineers, Python Developer and Cloud Engineers.
Collaborate with business stakeholders, product teams, and multiple clients to define requirements and deliver AI/ML/GenAI-driven solutions.
Conduct technical workshops, training sessions, and knowledge-sharing initiatives.
Multi-Client & Business Strategy
Manage multiple client engagements, delivering AI/ML/GenAI solutions tailored to their business needs.
Define AI/ML/GenAI roadmaps, proof-of-concept strategies, and go-to-market AI solutions.
Stay updated on cutting-edge AI advancements and drive innovation in AI/ML offerings.
Requirements
Key Skills & Technologies
Cloud & DevOps
AWS Services: Bedrock, SageMaker, Lambda, API Gateway, DynamoDB, S3, ECS, Fargate, OpenSearch, RDS
MLOps: SageMaker Pipelines, CI/CD (CodePipeline, GitHub Actions, Terraform, CDK)
Security: IAM, VPC, CloudTrail, GuardDuty, KMS, Cognito
AI/ML & GenAI
LLMs & Generative AI: Bedrock (Claude, Mistral, Titan), OpenAI, Llama
ML Frameworks: TensorFlow, PyTorch, LangChain, Hugging Face
Vector DBs: OpenSearch, Pinecone, FAISS
RAG Pipelines, Prompt Engineering, Fine-tuning
Software Architecture & Scalability
Serverless & Microservices Architecture
API Design & GraphQL
Event-Driven Systems (SNS, SQS, EventBridge, Step Functions)
Performance Optimization & Auto Scaling
Your next step
- Have your CV and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the salary range before committing time to the process.
Complete your application on neuralgo.zohorecruit.in. The employer’s form will show what is required.
Already applied? Track this application
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Location not supplied.
This is a remote position. Job Summary
More source context
Design Conversational AI, RAG (Retrieval-Augmented Generation), and Generative AI architectures using models like Claude (Anthropic), Mistral, Llama, and Titan. Optimize LLM inference pipelines, embeddings, vector search, and hybrid retrieval strategies for AI-based applications. Drive ML model training, deployment, and monitoring using AWS SageMaker and AI/ML pipelines.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
See how this role fits your experience
Add your resume to compare the role’s scope, tools and requirements with your experience.