ML Engineer / MLOps Engineer Job at InfoGrowth (Chennai)

ML Ops Engineer

As an MLOps Engineer, you will be responsible for building, maintaining, and opt...

Location

India , Hyderabad

Salary:

Not provided

NStarX

Expiration Date

Until further notice

Requirements

4 to 10 years of experience in MLOps, DevOps, or ML Engineering
Strong proficiency with cloud platforms such as AWS, Azure, or GCP
Experience with containerization and orchestration tools like Docker and Kubernetes
Hands-on experience with ML model deployment, monitoring, and scaling
Proficiency with CI/CD tools such as Jenkins or GitLab CI
Familiarity with data versioning and management tools such as DVC
Strong coding skills in Python with knowledge of ML libraries like TensorFlow or PyTorch
Strong problem-solving skills and ability to work in a collaborative environment
Effective communication skills for cross-functional teamwork

Job Responsibility

Develop and manage infrastructure for end-to-end ML workflows including model training, deployment, monitoring, and maintenance
Implement CI/CD pipelines for ML models and data workflows
Collaborate with cross-functional teams to build scalable and robust ML infrastructure on cloud and on-premises environments
Monitor and optimize model performance and infrastructure to ensure efficient resource usage
Manage data versioning and model versioning across multiple environments
Implement security, governance, and compliance protocols in ML deployment and data pipelines
Support troubleshooting, debugging, and incident management for ML infrastructure issues

What we offer

Competitive compensation
Opportunity to work with a dynamic team on cutting-edge AI and ML solutions
Professional growth and development opportunities

Fulltime

Senior ML Platform Engineer

At WHOOP, we're on a mission to unlock human performance and healthspan. WHOOP e...

Location

United States , Boston

Salary:

150000.00 - 210000.00 USD / Year

Whoop

Expiration Date

Until further notice

Requirements

Bachelor’s or Master’s Degree in Computer Science, Engineering, or a related field
or equivalent practical experience
5+ years of experience in software engineering with a focus on ML infrastructure, cloud platforms, or MLOps
Strong programming skills in Python, with experience in building distributed systems and REST/gRPC APIs
Deep knowledge of cloud-native services and infrastructure-as-code (e.g., AWS CDK, Terraform, CloudFormation)
Hands-on experience with model deployment platforms such as AWS SageMaker, Vertex AI, or Kubernetes-based serving stacks
Proficiency in ML lifecycle tools (MLflow, Weights & Biases, BentoML) and containerization strategies (Docker, Kubernetes)
Understanding of data engineering and ingestion pipelines, with ability to interface with data lakes, feature stores, and streaming systems
Proven ability to work cross-functionally with Data Science, Data Platform, and Software Engineering teams, influencing decisions and driving alignment
Passion for AI and automation to solve real-world problems and improve operational workflows

Job Responsibility

Architect, build, own, and operate scalable ML infrastructure in cloud environments (e.g., AWS), optimizing for speed, observability, cost, and reproducibility
Create, support, and maintain core MLOps infrastructure (e.g., MLflow, feature store, experiment tracking, model registry), ensuring reliability, scalability, and long-term sustainability
Develop, evolve, and operate MLOps platforms and frameworks that standardize model deployment, versioning, drift detection, and lifecycle management at scale
Implement and continuously maintain end-to-end CI/CD pipelines for ML models using orchestration tools (e.g., Prefect, Airflow, Argo Workflows), ensuring robust testing, reproducibility, and traceability
Partner closely with Data Science, Sensor Intelligence, and Data Platform teams to operationalize and support model development, deployment, and monitoring workflows
Build, manage, and maintain both real-time and batch inference infrastructure, supporting diverse use cases from physiological analytics to personalized feedback loops for WHOOP members
Design, implement, and own automated observability tooling (e.g., for model latency, data drift, accuracy degradation), integrating metrics, logging, and alerting with existing platforms
Leverage AI-powered tools and automation to reduce operational overhead, enhance developer productivity, and accelerate model release cycles
Contribute to and maintain internal platform documentation, SDKs, and training materials, enabling self-service capabilities for model deployment and experimentation
Continuously evaluate and integrate emerging technologies and deployment strategies, influencing WHOOP’s roadmap for AI-driven platform efficiency, reliability, and scale

What we offer

equity
benefits

Fulltime

ML Engineer

The international IT сompany Andersen invites a ML Engineer to join our dynamic ...

Location

Salary:

Not provided

Andersen

Expiration Date

Until further notice

Requirements

Experience as a Machine Learning Engineer or in a similar role for 3+ years
Proficiency in Python, including hands-on experience with libraries such as scikit-learn, pandas, NumPy, and matplotlib
Strong understanding of core ML concepts — regression, classification, clustering, model validation, and performance metrics
Practical experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras
Proven experience building, training, and deploying ML models using AWS SageMaker
Familiarity with AWS Bedrock for working with foundation and generative models (e.g., fine-tuning and orchestration of LLMs)
Hands-on experience with data preprocessing, feature engineering, and model evaluation
Knowledge of SQL and experience working with structured and semi-structured datasets
Understanding of ML model deployment (e.g., REST APIs with FastAPI or Flask
model packaging and containerization with Docker)

Job Responsibility

Designing, training, and evaluating machine learning models (supervised, unsupervised, NLP, etc.)
Building scalable data and ML pipelines using modern tools
Collaborating with subject matter experts and analysts to prepare training datasets
Deploying models for production (batch or real-time inference)
Monitoring and maintaining model performance and data quality
Optimizing models for performance, interpretability, and cost
Documenting ML workflows and ensuring reproducibility

What we offer

Experience in teamwork with leaders in FinTech, Healthcare, Retail, Telecom, and others
The opportunity to change the project and/or develop expertise in an interesting business domain
Guarantee of professional, financial, and career growth
The opportunity to earn up to an additional 1,000 USD per month, depending on the level of expertise, which will be included in the annual bonus, by participating in the company's activities
Access to the corporate training portal
Bright corporate life (parties / pizza days / PlayStation / fruits / coffee / snacks / movies)
Certification compensation (AWS, PMP, etc)
Referral program
English courses
Private health insurance and compensation for sports activities

Senior Software Engineer – ML Model Compliance & Automation

We are seeking a highly skilled and motivated Senior Software Engineer to lead t...

Location

India , Jaipur

Salary:

Not provided

InfoObjects

Expiration Date

Until further notice

Requirements

Experience Required: 3 - 7 yrs
GoLang (preferred)
Python (preferred)
Bash
MLOps Tools: KitOps, MLModelCI, MLflow, ONNX, TensorFlow, PyTorch, Docker
SBOM & Security: Syft, Grype, Trivy, CycloneDX, SPDX
CI/CD: GitHub Actions, GitLab CI, Jenkins, ArgoCD
Infra: Kubernetes, Docker, Helm, Terraform
Cloud: AWS, GCP, Azure (EKS/GKE/ECS preferred)
Version Control: Git, GitOps

Job Responsibility

Model Packaging & Artifact Management: Design and implement workflows for packaging ML models using KitOps, ONNX, MLflow, or TensorFlow SavedModel
Manage model artifact versioning, registries, and reproducibility
Ensure artifact integrity, consistency, and traceability across CI/CD pipelines
Model Profiling & Optimization: Automate model profiling (latency, size, ops) using MLModelCI, TorchServe, or ONNX Runtime
Apply quantization, pruning, and format conversions (e.g., FP32→INT8) for optimization
Embed profiling and optimization checks into CI/CD pipelines to assess deployment readiness
Compliance & SBOM Generation: Develop pipelines to generate and validate SBOMs for ML models
Implement compliance checks for licensing, vulnerabilities, and security using CycloneDX, SPDX, Syft, or Trivy
Validate schema, dependencies, and runtime environments for production readiness
Cloud Integration & Deployment: Automate model registration, endpoint creation, and monitoring setup in AWS/GCP/Azure

Fulltime

Senior AI ML Engineer

We are seeking a highly skilled and experienced Assistant Vice President (AVP), ...

Location

India , Pune

Salary:

Not provided

Citi

Expiration Date

Until further notice

Requirements

Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Statistics, or a related quantitative field
Minimum of 6+ years of professional experience in Data Science, Machine Learning Engineering, or a similar role, with a strong track record of deploying ML models to production
Proven experience in a lead or senior technical role
Expert-level proficiency in Python programming, including experience with relevant data science libraries (e.g., Pandas, NumPy, Scikit-learn) and deep learning frameworks (e.g., TensorFlow, PyTorch)
Strong hands-on experience designing, developing, and deploying RESTful APIs using FastAPI
Solid understanding and practical experience with CI/CD tools and methodologies (e.g., Jenkins, GitLab CI, GitHub Actions, Azure DevOps) for MLOps
Experience with MLOps platforms, model monitoring, and model versioning
Experience with at least one major cloud provider (e.g., AWS, Azure, GCP) for deploying and managing ML workloads
Proficiency in SQL and experience working with relational and/or NoSQL databases
Deep understanding of machine learning algorithms, statistical modeling, and data mining techniques

Job Responsibility

Design, develop, and implement advanced machine learning models (e.g., predictive, prescriptive, generative AI) to solve complex business problems, from initial data exploration and feature engineering to model training and evaluation
Lead the deployment of AI/ML models into production environments, ensuring scalability, reliability, and performance
Build and maintain robust, high-performance APIs (using frameworks like FastAPI) to serve machine learning models and integrate them with existing applications and systems
Establish and manage continuous integration and continuous deployment (CI/CD) pipelines for ML code and model deployments, promoting automation and efficiency
Collaborate with data engineers to ensure optimal data pipelines and data quality for model development and deployment
Conduct rigorous experimentation, A/B testing, and model performance monitoring to continuously improve and optimize AI/ML solutions
Promote and enforce best practices in software development, including clean code, unit testing, documentation, and version control
Mentor junior team members, contribute to technical discussions, and drive the adoption of new technologies and methodologies within the team
Effectively communicate complex technical concepts and model results to both technical and non-technical stakeholders.

What we offer

Not explicitly stated.

Fulltime

Staff MLOps Engineer

At Inworld, we’re building the AI framework behind the next generation of real-t...

Location

Canada , Vancouver

Salary:

190000.00 - 240000.00 CAD / Year

Inworld AI

Expiration Date

Until further notice

Requirements

7+ years of software engineering experience
5+ years of infrastructure-as-code
Proficiency in managing Kubernetes clusters and applications, including creating Helm charts/Kustomize manifests for new applications
Experience in creating and maintaining CI/CD pipelines for both applications and infrastructure deployments (using tools like Terraform/Terragrunt, ArgoCD, GitHub Actions, Ansible, etc.)
Deep knowledge of at least one major cloud provider (Google Cloud Platform, Microsoft Azure, Oracle Cloud)
Proficient in at least one backend programming/scripting languages such as Golang, Python, and Bash
Knowledge of SLURM or similar job schedulers for distributed training
Experience with data pipeline and workflow management tools
Desire to work at a fast-growing Series A startup, comfortable with uncertainty, owning and scaling new products, and embracing an experimental and iterative development process

Job Responsibility

Build and scale MLOps systems to streamline the end-to-end ML model lifecycle on the Inworld AI platform, from training to deployment
Design and implement robust model training, evaluation, and release pipelines
Collaborate cross-functionally with ML and backend teams to design, deploy, and maintain scalable secure infrastructure for Inworld’s AI Engine and Studio
Facilitate a "you build it, you run it" culture by providing the necessary tools and processes for monitoring the reliability, availability, and performance of services
Manage CI/CD pipelines to ensure smooth and efficient code integration and deployment
Identify and implement opportunities to enhance engineering speed and efficiency
Provide technical leadership in ML engineering best practices, raise the technical bar, and mentor junior engineers in MLOps principles

What we offer

equity
benefits

Fulltime

Staff MLOps Engineer

At Inworld, we’re building the AI framework behind the next generation of real-t...

Location

United States , Mountain View

Salary:

180000.00 - 280000.00 USD / Year

Inworld AI

Expiration Date

Until further notice

Requirements

7+ years of software engineering experience, with 5+ years of infrastructure-as-code
Proficiency in managing Kubernetes clusters and applications, including creating Helm charts/Kustomize manifests for new applications
Experience in creating and maintaining CI/CD pipelines for both applications and infrastructure deployments (using tools like Terraform/Terragrunt, ArgoCD, GitHub Actions, Ansible, etc.)
Deep knowledge of at least one major cloud provider (Google Cloud Platform, Microsoft Azure, Oracle Cloud)
Proficient in at least one backend programming/scripting languages such as Golang, Python, and Bash
Knowledge of SLURM or similar job schedulers for distributed training
Experience with data pipeline and workflow management tools
Desire to work at a fast-growing Series A startup, comfortable with uncertainty, owning and scaling new products, and embracing an experimental and iterative development process
In-office location: Mountain View, CA, United States. You must be available for hybrid work

Job Responsibility

Build and scale MLOps systems to streamline the end-to-end ML model lifecycle on the Inworld AI platform, from training to deployment
Design and implement robust model training, evaluation, and release pipelines
Collaborate cross-functionally with ML and backend teams to design, deploy, and maintain scalable secure infrastructure for Inworld’s AI Engine and Studio
Facilitate a "you build it, you run it" culture by providing the necessary tools and processes for monitoring the reliability, availability, and performance of services
Manage CI/CD pipelines to ensure smooth and efficient code integration and deployment
Identify and implement opportunities to enhance engineering speed and efficiency
Provide technical leadership in ML engineering best practices, raise the technical bar, and mentor junior engineers in MLOps principles

What we offer

equity and benefits

Fulltime

MLOps Engineer

As an MLOps Engineer, you will help our clients with automating and managing the...

Location

Belgium , Brussels

Salary:

Not provided

Sopra Steria

Expiration Date

Until further notice

Requirements

At least 3 years of experience as an MLOps Engineer, Machine Learning Engineer or in a similar position
Experience managing the machine learning lifecycle in production, and related MLOps frameworks
Master CI/CD pipelines and Infrastructure as Code
In depth knowledge of containers and container orchestration
Solid understanding of Linux and in depth knowledge of at least one cloud provider
Programming experience with Python
Experience with monitoring and logging technologies
Good grasp of big data technologies such as Apache Spark, ElasticSearch, Kafka
Able to coach others and give technical advice and direction
Understanding of networking, firewalls, and IT infrastructure

Job Responsibility

Design and implement solutions to automate the experimentation and release cycle of machine learning, from traditional ML to LLMs
Identify, design, and implement process improvements in the machine learning lifecycle: automating manual processes, optimizing data delivery, re-design infrastructure for greater scalability, etc
Collaborate with Data Scientists, Machine Learning Engineers, Data Engineers, and other IT roles to integrate all parts of the solution
Implement CI/CD pipelines, IaC, monitoring for models and infrastructure

What we offer

A variety of perks, such as mobility options (including a company car), insurance coverage, meal vouchers, eco-cheques, and more
Continuous learning opportunities through the Sopra Steria Academy to support your career development
The opportunity to connect with fellow Sopra Steria colleagues at various team events

Fulltime

ML Engineer / MLOps Engineer

InfoGrowth

Location:
India , Chennai

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Job Description:

Job Responsibility:

Requirements:

Nice to have:

Additional Information:

Job Posted:
April 11, 2026

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