
Review Criteria:
- Strong MLOps profile
- 8+ years of DevOps experience and 4+ years in MLOps / ML pipeline automation and production deployments
- 4+ years hands-on experience in Apache Airflow / MWAA managing workflow orchestration in production
- 4+ years hands-on experience in Apache Spark (EMR / Glue / managed or self-hosted) for distributed computation
- Must have strong hands-on experience across key AWS services including EKS/ECS/Fargate, Lambda, Kinesis, Athena/Redshift, S3, and CloudWatch
- Must have hands-on Python for pipeline & automation development
- 4+ years of experience in AWS cloud, with recent companies
- (Company) - Product companies preferred; Exception for service company candidates with strong MLOps + AWS depth
Preferred:
- Hands-on in Docker deployments for ML workflows on EKS / ECS
- Experience with ML observability (data drift / model drift / performance monitoring / alerting) using CloudWatch / Grafana / Prometheus / OpenSearch.
- Experience with CI / CD / CT using GitHub Actions / Jenkins.
- Experience with JupyterHub/Notebooks, Linux, scripting, and metadata tracking for ML lifecycle.
- Understanding of ML frameworks (TensorFlow / PyTorch) for deployment scenarios.
Job Specific Criteria:
- CV Attachment is mandatory
- Please provide CTC Breakup (Fixed + Variable)?
- Are you okay for F2F round?
- Have candidate filled the google form?
Role & Responsibilities:
We are looking for a Senior MLOps Engineer with 8+ years of experience building and managing production-grade ML platforms and pipelines. The ideal candidate will have strong expertise across AWS, Airflow/MWAA, Apache Spark, Kubernetes (EKS), and automation of ML lifecycle workflows. You will work closely with data science, data engineering, and platform teams to operationalize and scale ML models in production.
Key Responsibilities:
- Design and manage cloud-native ML platforms supporting training, inference, and model lifecycle automation.
- Build ML/ETL pipelines using Apache Airflow / AWS MWAA and distributed data workflows using Apache Spark (EMR/Glue).
- Containerize and deploy ML workloads using Docker, EKS, ECS/Fargate, and Lambda.
- Develop CI/CT/CD pipelines integrating model validation, automated training, testing, and deployment.
- Implement ML observability: model drift, data drift, performance monitoring, and alerting using CloudWatch, Grafana, Prometheus.
- Ensure data governance, versioning, metadata tracking, reproducibility, and secure data pipelines.
- Collaborate with data scientists to productionize notebooks, experiments, and model deployments.
Ideal Candidate:
- 8+ years in MLOps/DevOps with strong ML pipeline experience.
- Strong hands-on experience with AWS:
- Compute/Orchestration: EKS, ECS, EC2, Lambda
- Data: EMR, Glue, S3, Redshift, RDS, Athena, Kinesis
- Workflow: MWAA/Airflow, Step Functions
- Monitoring: CloudWatch, OpenSearch, Grafana
- Strong Python skills and familiarity with ML frameworks (TensorFlow/PyTorch/Scikit-learn).
- Expertise with Docker, Kubernetes, Git, CI/CD tools (GitHub Actions/Jenkins).
- Strong Linux, scripting, and troubleshooting skills.
- Experience enabling reproducible ML environments using Jupyter Hub and containerized development workflows.
Education:
- Master’s degree in computer science, Machine Learning, Data Engineering, or related field.

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Experience - 12+ yrs
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Wekan Enterprise Solutions is a leading Technology Consulting company and a strategic investment partner of MongoDB. We help companies drive innovation in the cloud by adopting modern technology solutions that help them achieve their performance and availability requirements. With strong capabilities around Mobile, IOT and Cloud environments, we have an extensive track record helping Fortune 500 companies modernize their most critical legacy and on-premise applications, migrating them to the cloud and leveraging the most cutting-edge technologies.
Job Description
We are looking for passionate architects eager to be a part of our growth journey. The right candidate needs to be interested in working in high-paced and challenging environments leading technical teams, designing system architecture and reviewing peer code. Interested in constantly upskilling, learning new technologies and expanding their domain knowledge to new industries. This candidate needs to be a team player and should be looking to help build a culture of excellence. Do you have what it takes?
You will be working on complex data migrations, modernizing legacy applications and building new applications on the cloud for large enterprise and/or growth stage startups. You will have the opportunity to contribute directly into mission critical projects directly interacting with business stakeholders, customer’s technical teams and MongoDB solutions Architects.
Location - Chennai or Bangalore
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● Good problem solving skills
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● Strong experience writing and maintaining clear documentation
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● Extensive experience consulting with customers directly for defining architecture or system design.
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Budget- 20 to 24LPA
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Mandatory Criteria
- You are an MBA grad with 5+ years into your marketing career, with strong B2B experience.
- You are equally comfortable thinking and doing — you don’t just manage, you make things happen
- You think like a founder — practical, agile, and resourceful.
- You thrive in fast-paced, iterative environments.
- You are fascinated by what makes AI and Learning tick.
- You value output over optics — and clarity over jargon.
OKR
● Own the marketing calendar and deliver practical, sharp B2B campaigns that generate
sales meetings and conversations, not just clicks and impressions.
● Come up with breakthrough marketing initiatives that challenge “this is how it’s done”
thinking.
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approach
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If interested kindly share your updated resume at 82008 31681
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Company: Translense Private Limited
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Translense Private Limited is a growing technology company focused on building practical and powerful digital solutions for the beauty and wellness industry. Our flagship product, Styller, empowers salons and spas to digitize bookings, increase online visibility, and manage operations efficiently.
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