Machine Learning Architect

goML; United States; Remote; USD 120,000 - 160,000; Full time

Job summary

A leading company is seeking a highly skilled Technical Architect to design and implement scalable AI/ML solutions on AWS. The ideal candidate will have extensive experience in cloud architecture, technical leadership, and managing client engagements. This role requires expertise in Generative AI, MLOps, and DevOps, along with a strong ability to collaborate with cross-functional teams. Join us to drive innovation in AI and cloud technologies.

Qualifications

• 9-12 years of experience in technical architecture.
• Proven track record in managing multiple clients and leading teams.

Responsibilities

• Design and implement scalable architectures on AWS for AI/ML applications.
• Lead full lifecycle development of AI/ML/GenAI solutions.
• Manage multiple client engagements and deliver tailored solutions.

Skills

AWS

AI/ML

Generative AI

Cloud Architecture

MLOps

DevOps

Python

Tools

SageMaker

Terraform

CDK

TensorFlow

PyTorch

Job description

Job Summary

We are seeking a highly skilled Technical Architect with expertise in AWS, Generative AI, AI/ML, and scalable production architectures. The ideal candidate should have 9-12 years of experience, with a proven track record of managing multiple clients, leading technical teams, and designing end-to-end cloud-based AI solutions.

This role involves architecting AI/ML/GenAI applications, ensuring best practices in cloud deployment, security, and scalability, while collaborating with cross-functional teams.

Key Responsibilities

1. 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 the 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.

1. 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.

1. 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.

1. Team & Client Engagement

• Lead and mentor a team of ML engineers, Python developers, and Cloud engineers.
• Collaborate with business stakeholders, product teams, and multiple clients to define requirements and deliver AI/ML/GenAI solutions.
• Conduct technical workshops, training sessions, and share knowledge.

1. Multi-Client & Business Strategy

• Manage multiple client engagements, delivering tailored AI/ML/GenAI solutions.
• Define AI/ML/GenAI roadmaps, proof-of-concept strategies, and go-to-market solutions.
• Stay updated on AI advancements and foster innovation.

Key Skills & Technologies

• Cloud & DevOps: AWS services (Bedrock, SageMaker, Lambda, API Gateway, DynamoDB, S3, ECS, Fargate, OpenSearch, RDS), MLOps tools (SageMaker Pipelines, CI/CD, Terraform, CDK), Security (IAM, VPC, CloudTrail, GuardDuty, KMS, Cognito)
• AI/ML & GenAI: LLMs (Claude, Mistral, Titan, OpenAI, Llama), Frameworks (TensorFlow, PyTorch, LangChain, Hugging Face), Vector DBs (OpenSearch, Pinecone, FAISS), RAG pipelines, Prompt engineering, Fine-tuning
• Software Architecture: Scalability, Serverless, Microservices, API Design, GraphQL, Event-driven systems, Performance optimization, Auto Scaling