Machine Learning Engineer

Role Overview

We are looking for an experienced

MLOps Lead

with deep expertise in

Azure and AWS cloud ecosystems , who can design, deploy, and manage scalable AI / ML infrastructure. The ideal candidate should bring a strong background in

cloud governance, GenAI tooling, automation, and CI / CD pipelines , with hands-on experience across modern MLOps frameworks.

Key Responsibilities

Design, implement, and manage scalable cloud-based AI / ML infrastructure across

Azure and AWS .

Drive

end-to-end MLOps lifecycle

— model deployment, monitoring, retraining, and governance.

Enable

GenAI and Agentic AI platforms

leveraging Azure OpenAI, Bedrock, Anthropic Claude, LangChain, etc.

Implement

CI / CD pipelines

using Azure DevOps or AWS CodePipeline.

Ensure

security, observability, and compliance

across ML and GenAI ecosystems.

Manage infrastructure automation via

Terraform, Bicep, CloudFormation , or similar IaC tools.

Collaborate with data science and engineering teams to optimize ML workflows, data pipelines, and API integrations.

Implement

monitoring and alerting

using Grafana, Prometheus, Azure Monitor, and Application Insights.

Oversee

networking, identity management, and role-based access controls (IAM, RBAC)

across clouds.

Support model lifecycle management —

drift monitoring, retraining, technical evaluation, and business validation.

Technical Skills & Expertise

Cloud & MLOps Platforms

Azure :

Azure ML, Azure AI Services, Azure OpenAI, Azure Kubernetes Service (AKS), Databricks, Azure Search, Azure Blob, Cosmos DB, Azure SQL, Azure Functions, Azure Event Hub, Azure Resource Manager (ARM), Bicep.

AWS :

SageMaker, Bedrock, Lambda, DynamoDB, S3, RDS, Redshift, ECR, CloudFormation, CDK, KMS, EventBridge, Step Functions.

AI / ML & Programming

Hands-on in

Python , with exposure to TensorFlow, PyTorch, scikit-learn.

Understanding of

LLM tokenization, prompt injection risks, jailbreak prevention, and AI safety techniques.

Familiarity with

LangChain, LlamaCloud, AI Foundry , and related frameworks.

Experience in

model monitoring, retraining, and evaluation workflows.

DevOps & Infrastructure

Expertise in

CI / CD pipelines ,

containerization (Docker, Kubernetes) , and

infrastructure automation .

Strong in

governance, audit logging, security policies

(Azure Policy, AWS SCP, IAM).

Deep understanding of

networking, DNS, load balancers, VNets / VPCs, VPNs.

Skilled in

IaC

tools – Terraform, Bicep, ARM, CloudFormation.

Monitoring & Observability

Experience with

Grafana, Prometheus, Application Insights, Log Analytics Workspaces, Azure Monitor.

Security & Access Management

Understanding of

Microsoft AD, least privilege principles, IAM, RBAC.

Testing & Automation

Familiarity with

unit testing and integration testing

in CI / CD workflows (preferably Azure DevOps).

Good to Have

Experience with

Azure Bot Framework ,

M365 Copilot , and

APIM .

Exposure to

code assistants

such as GitHub Copilot, Cursor, Claude Code.

Knowledge of

Boto3 SDK (AWS Python)

and

TypeScript for IaC .

Preferred Background

Strong background in

cloud infrastructure engineering

and

machine learning operations .

Proven ability to lead

cross-functional teams

and implement

AI governance

at scale.

Excellent problem-solving, communication, and documentation skills.