Machine Learning Architect
Posted Updated 2
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Summary
Senior (9-12 yrs) technical architect designing end-to-end AWS-based AI/ML and Generative AI solutions for multiple clients, from PoC to production. Works with SageMaker, Bedrock, Lambda and LLMs like Claude, Mistral and Llama, while mentoring engineers and driving MLOps best practices.
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 overallexperience 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
Skills
- AI
- Anthropic
- API
- Api Design
- API Gateway
- AWS
- CDK
- CI/CD
- Cloud
- CloudTrail
- Conversational AI
- DataOps
- DevOps
- DynamoDB
- ECS
- Embeddings
- Event Driven Architecture
- Eventbridge
- FAISS
- Fargate
- Fine Tuning
- Generative AI
- GitHub
- GitHub Actions
- Go-to-Market
- GraphQL
- Hugging Face
- IAM
- Lambda
- LangChain
- LLM
- Machine Learning
- Microservices
- Mistral
- MLOps
- OpenAI
- OpenSearch
- Pinecone
- Prompt Engineering
- Proof of Concept
- Python
- PyTorch
- RAG
- RDS
- S3
- SageMaker
- Serverless
- SNS
- SQS
- TensorFlow
- Terraform
- Vector Search
- VPC