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Posted 11mo ago

Machine Learning Architect

@ Neuralgo
India
RemoteFull Time
Responsibilities:Lead architecture, Mentor engineers, Collaborate with clients
Requirements Summary:5+ years experience in AWS/AI/ML/GenAI; lead technical teams; design end-to-end cloud-based AI solutions; remote work.
Technical Tools Mentioned:AWS Bedrock, AWS SageMaker, AWS Lambda, API Gateway, DynamoDB, S3, ECS, Fargate, OpenSearch, RDS, SageMaker Pipelines, CodePipeline, GitHub Actions, Terraform, CDK, IAM, VPC, GuardDuty, KMS, Cognito, TensorFlow, PyTorch, LangChain, Hugging Face, OpenAI, Llama, Bedrock (Claude), Mistral, Titan, Vector DBs, OpenSearch, Pinecone, FAISS
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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