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Machine Learning Engineer

Machine Learning Engineer

Recro

0 years

0 Lacs

india

Posted:10 hours ago| Platform:

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Skills Required

learning azure aws design ai ml governance tooling automation drive model deployment monitoring openai devops security terraform data engineering api networking management controls iam support drift evaluation kubernetes service databricks sql sagemaker dynamodb redshift tensorflow pytorch tokenization containerization docker audit logging analytics testing integration code github typescript communication documentation

Work Mode

On-site

Job Type

Full Time

Job Description

Role Overview

MLOps Lead

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.

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