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 explicit 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

📌 AI Architect (Panipat)
🏢 goML
📍 Panipat