MLOps Engineer (Python Backend + AIGenAI Experience)

Hyderabad - India

Monthly Salary: Not Disclosed

Posted on: 6 hours ago

Vacancies: 1 Vacancy

Job Summary

We are looking for a senior MLOps Engineer with strong Python backend engineering expertise to design build and manage scalable ML and AI platforms. The ideal candidate has hands-on experience with AWS SageMaker ML pipelines Infrastructure as Code GenAI/RAG workflows and containerized deployments.

You will collaborate closely with Data Scientists ML Engineers and AI Engineers to build robust pipelines automate workflows deploy models at scale and support end-to-end ML lifecycle in production.

Key Responsibilities

MLOps & ML Pipeline Engineering

Build maintain and optimize ML pipelines in AWS (SageMaker Lambda Step Functions ECR S3).
Manage model training evaluation versioning deployment and monitoring using MLOps best pratices.
Implement CI/CD for ML workflows using GitHub Actions / CodePipeline / GitLab CI.
Set up and maintain Infrastructure as Code (IaC) using CloudFormation or Terraform.

Backend Engineering (Python)

Design and build scalable backend services using Python (FastAPI/Flask).
Build APIs for model inference feature retrieval data access and microservices.
Develop automation scripts SDKs and utilities to streamline ML workflows.

AI/GenAI & RAG Workflows (Good to Have / Nice to Have)

Implement RAG pipelines vector indexing and document retrieval workflows.
Build and deploy multi-agent systems using frameworks like LangChain CrewAI or Google ADK.
Apply prompt engineering strategies for optimizing LLM behavior.
Integrate LLMs with existing microservices and production data.

Model Deployment & Observability

Deploy models using Docker Kubernetes (EKS/ECS) or SageMaker endpoints.
Implement monitoring for model drift data drift usage patterns latency and system health.
Maintain logs metrics and alerts using CloudWatch Prometheus Grafana or ELK.

Collaboration & Documentation

Work directly with data scientists to support experiments deployments and re-platforming efforts.
Document design decisions architectures and infrastructure using Confluence GitHub Wikis or architectural diagrams.
Provide guidance and best practices for reproducibility scalability and cost optimization.

Qualifications :

Must-Have

5 years total experience with at least 3 years in MLOps/ML Engineering.
Hands-on experience deploying at least two MLOps projects using AWS SageMaker or equivalent cloud services.
Strong backend engineering foundation in Python (FastAPI Flask Django).
Deep experience with AWS services: SageMaker ECR S3 Lambda Step Functions CloudWatch.
Strong proficiency in Infrastructure as Code: CloudFormation / Terraform.
Strong understanding of ML lifecycle model versioning monitoring and retraining.
Experience with Docker GitHub Actions Git-based workflows CI/CD pipelines.
Experience working with RDBMS/NoSQL API design and microservices.

Good to Have

Experience building RAG pipelines vector stores (FAISS Pinecone) or embeddings workflows.
Experience with agentic systems (LangChain CrewAI Google ADK).
Understanding of data security privacy and compliance frameworks.
Exposure to Databricks Airflow or Spark-based pipelines.
Multi-cloud familiarity (Azure/GCP AI services).

Soft Skills

Strong communication skills able to collaborate with cross-functional teams.
Ability to work independently and handle ambiguity.
Analytical thinker with strong problem-solving skills.
Ownership mindset with focus on delivery and accountability.

Additional Information :

Our Perks and Benefits:

Learning Opportunities:

Certifications in AWS (we are AWS Partners) Databricks and Snowflake.
Access to AI learning paths to stay up to date with the latest technologies.
Study plans courses and additional certifications tailored to your role.
Access to Udemy Business offering thousands of courses to boost your technical and soft skills.
English lessons to support your professional communication.

Mentoring and Development:

Career development plans and mentorship programs to help shape your path.

Celebrations & Support:

Special day rewards to celebrate birthdays work anniversaries and other personal milestones.
Company-provided equipment.

Flexible working options to help you strike the right balance.

Remote Work :

Employment Type :

Full-time

We are looking for a senior MLOps Engineer with strong Python backend engineering expertise to design build and manage scalable ML and AI platforms. The ideal candidate has hands-on experience with AWS SageMaker ML pipelines Infrastructure as Code GenAI/RAG workflows and containerized deployments.Yo...