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Associate Machine Learning Architect

Associate Machine Learning Architect

goMLKakinada, IN
18 days ago
Job description

Architect the Future of AI with goML

At goML , we design and build cutting-edge Generative AI, AI / ML solutions that help enterprises unlock the full potential of their data, drive intelligent automation, and create transformative AI-powered experiences.

Our mission is to bridge the gap between advanced AI research and real-world enterprise adoption — empowering businesses to innovate faster, make smarter decisions, and scale AI seamlessly across operations.

We’re looking for an Associate ML Architect with strong expertise in AWS, Generative AI, and ML system design . In this role, you’ll architect scalable, high-performance, and cost-efficient AI solutions — leveraging AWS AI / ML services, modern data platforms, and cloud-native best practices.

If you’re passionate about building production-grade AI systems and driving the next wave of enterprise GenAI innovation, we’d love to hear from you!

Why You? Why Now?

Generative AI is revolutionizing how businesses operate — but scaling it requires strong architecture, security, and performance foundations.

This role is ideal for someone who loves architecting AI / ML workloads , optimizing LLM pipelines , and collaborating with clients to deliver enterprise-grade AI systems.

At goML, You Will :

  • Architect and own end-to-end AI / ML and GenAI solutions across industries.
  • Collaborate with sales, presales, and engineering teams to define technical proposals.
  • Design cloud-native, multi-tenant, and serverless architectures on AWS using services like SageMaker, Bedrock, Lambda, API Gateway, and OpenSearch.
  • Work directly with the co-founders and leadership to influence AI / ML strategy and architecture decisions.

What You’ll Do (Key Responsibilities)

First 30 Days : Foundation & Alignment

  • Deep dive into goML’s AI / ML & GenAI solution frameworks and ongoing client architectures.
  • Familiarize yourself with AWS partnership workflows and solution templates.
  • Partner with presales teams to understand client needs and pain points.
  • Start designing and reviewing AI / ML workload deployment practices on AWS.

    • First 60 Days : Execution & Impact

  • Own solution design and delivery for client AI / ML and LLM workloads.
  • Build reference architectures, POCs , and define ML pipelines with MLOps best practices.
  • Optimize inference, embeddings, GPU utilization, and cost efficiency across workloads.
  • Collaborate with clients and internal teams to translate GenAI use cases into scalable architectures.

    • First 180 Days : Ownership & Leadership

  • Lead architecture reviews for enterprise-grade AI / ML systems.
  • Implement RAG pipelines , fine-tuning strategies, and LLM optimization frameworks.
  • Mentor ML engineers and cloud developers in MLOps and scalable AI system design.
  • Contribute to goML’s AI / ML best practices , internal accelerators, and knowledge base.
  • Represent goML in technical blogs, webinars, and community forums.

    • What You Bring (Qualifications & Skills)

    Must-Have

  • 6–7 years of experience in AI / ML solution design and deployment on AWS.
  • Proven expertise in AWS AI / ML stack – SageMaker, Bedrock, Lambda, ECS, API Gateway, S3, and DynamoDB.
  • Experience in LLMOps, RAG pipelines, and GenAI architecture using models like Claude, Mistral, Llama, or Titan.
  • Solid understanding of MLOps , CI / CD, and infrastructure automation (Terraform / CDK).
  • Strong communication skills with experience in client-facing discussions and technical proposal creation.

    • Nice-to-Have

  • Exposure to Azure ML, GCP Vertex AI, or NVIDIA AI / ML services.
  • Hands-on experience with LangChain, Hugging Face , or vector databases (OpenSearch, Pinecone, FAISS).
  • Familiarity with multi-cloud AI deployments and hybrid data architectures.

    • Why Work With Us?

  • Remote-first , with offices in Coimbatore for collaboration and innovation.
  • Opportunity to build GenAI and AI / ML systems that power enterprise-scale transformations.
  • Direct impact on technical strategy, solution architecture, and client success.
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