Machine Learning Architect

At goML, we design and build cutting-edge Generative AI, AI/ML, and Data Engineering solutions that help businesses unlock the full potential of their data, drive intelligent automation, and create transformative AI-powered experiences. Our mission is to bridge the gap between state-of-the-art AI research and real-world enterprise applications—helping organizations innovate faster, make smarter decisions, and scale AI solutions seamlessly.

We’re looking for a highly skilled Technical Architect with deep expertise in AWS, Generative AI, AI/ML, and scalable production-level architectures. In this role, you’ll lead end-to-end AI solution architecture—from PoC to enterprise-scale production, drive cloud security and scalability best practices, and work closely with multiple clients and internal delivery teams. If you love architecting robust systems, mentoring engineering teams, and building GenAI solutions that actually ship—we’d love to hear from you.

Why You? Why Now?

Enterprises are moving beyond experimentation and pushing GenAI into real production systems. That requires architects who can think beyond models and prototypes—someone who can design secure, scalable, multi-tenant AI solutions with clear MLOps foundations and cloud-native best practices.

This role is ideal for a leader who

• owns architectures end-to-end (not just diagrams)
• can manage multiple clients / multiple programs
• drives best practices in MLOps, DevOps, and cloud security
• brings strong technical leadership and mentoring capabilities

What You’ll Do (Key Responsibilities)

First 30 Days: Foundation & Architecture Alignment

• Deep dive into goML’s GenAI/AI/ML delivery framework, reference architectures, and deployment standards
• Understand ongoing customer engagements, solution maturity, and production constraints
• Review current AWS architecture patterns used across projects
• Align with stakeholders on delivery expectations, system SLAs, security requirements, and scalability goals
• Start contributing to solution planning, cloud design decisions, and technical estimation

First 60 Days: Execution & Impact

Own the architecture of AI/ML and GenAI solutions end-to-end

• requirement analysis
• cloud architecture design
• implementation guidance
• deployment readiness

Design multi-tenant, enterprise-grade AI systems using AWS services such as

SageMaker, Bedrock, Lambda, API Gateway, DynamoDB, ECS/Fargate, S3, OpenSearch, Step Functions

Implement best practices for

MLOps + model lifecycle

DataOps

DevOps pipelines

Drive Conversational AI / RAG implementations

• embeddings & retrieval strategies
• vector search + hybrid retrieval
• inference optimization and cost tuning
• Collaborate closely with product, engineering, data science, and client teams through architecture reviews and workshops

First 180 Days: Ownership & Transformation

Lead full lifecycle AI architecture—from PoC to production—with reliability and performance focus

Design and guide implementation of

• event-driven architectures
• serverless & microservices systems for AI workloads
• scalable API layers and orchestration flows

Ensure security, compliance, and governance

• IAM + VPC best practices
• auditability
• security guardrails and monitoring

Own cost and performance optimization across AI workloads

• inference compute optimization
• vector database tuning
• autoscaling strategies

Mentor and build strong technical teams

• ML engineers
• Python developers
• cloud engineers

Drive client strategy

• roadmaps
• go-to-market AI offerings
• solution proposals and long-term innovation

What You Bring (Qualifications & Skills)

• ✅ Must-Have
• 9–12 years of overall experience, with strong background in technical architecture and cloud solutions
• Proven experience designing and delivering production-grade AI/ML and GenAI applications

Strong hands-on expertise across AWS services, especially

Bedrock, SageMaker, Lambda, API Gateway, DynamoDB, S3, ECS/Fargate, OpenSearch, RDS

Deep knowledge of cloud-native architecture patterns

• microservices
• event-driven systems
• serverless architecture
• Proven ability to lead technical teams and mentor engineers

Strong client-facing skills

• requirement gathering
• architecture walkthroughs
• solution presentations
• stakeholder alignment
• Experience managing multiple client engagements or parallel deliveries
• ⭐ Nice-to-Have
• Experience with GraphQL API design and advanced enterprise integration patterns
• Exposure to multi-cloud environments (AWS + Azure/GCP)
• Strong background in building reusable frameworks/platform accelerators for GenAI delivery

Core Technology Stack

Cloud, DevOps & Security

• AWS: Bedrock, SageMaker, Lambda, API Gateway, DynamoDB, S3, ECS, Fargate, OpenSearch, RDS
• MLOps/DevOps: SageMaker Pipelines, CI/CD (CodePipeline, GitHub Actions), Terraform, AWS CDK

Security: IAM, VPC, CloudTrail, GuardDuty, KMS, Cognito

AI/ML & Generative AI

LLMs: Bedrock (Claude, Mistral, Titan), OpenAI, Llama

Frameworks: TensorFlow, PyTorch, LangChain, Hugging Face

Vector DBs: OpenSearch, Pinecone, FAISS

Concepts: RAG pipelines, prompt engineering, fine-tuning, embeddings, inference optimization

Architecture & Scalability

• Serverless + microservices architectures
• Performance optimization & autoscaling

Event-driven systems

SNS, SQS, EventBridge, Step Functions

• API design, scalability and resilience engineering
• Why Work With Us?
• Build cutting-edge GenAI architectures that go beyond demos—into real production
• Work with multiple enterprise clients across industries and use cases
• High ownership + high impact environment with strong engineering culture
• Remote-first, with offices in Coimbatore for in-person collaboration
• Competitive compensation, career growth, and ESOP opportunities