Lead MLOps Engineer and AI Solution Architect

Hello everyone,

Greetings for the day

We are looking out for MLOps Engineers and AI Solution Architects for Mumbai, Pune and Chennai locations.

1. Lead MLOPs Engineer.

Work experience: 5-8 years

Location: Mumbai, Pune and Chennai.

Job Description

As an  Technical Lead – MLOps,  you will be a part of an Agile team to build healthcare applications and implement new features while adhering to the best coding development standards

Your key responsibilities include:

·         Monitoring Build & Production systems using automated monitoring and alarm tools

·         Creating and using benchmarks, metrics, and monitoring to measure and improve services.

·         Providing best practices and executing POC for automated and efficient model operations at scale.

·         Designing and developing scalable MLOps frameworks to support models based on client requirements.

Mandatory Skills

·        Good hands-on experience & in-depth knowledge ML flow, MLOPs

·        Experience on AWS Sagemaker - Expected to manage database and installations

·        Good exposure to AWS cloud deployments

·        Real-World Hands-On Experience in MLOps and Data science field

·        Requirement understanding and effectively manage client communication 

Desired Skills

·        Good experience in GIT, Agile (Scrum or Kanban)

·        Flexible to work in evening timings  

·        Good to have (added advantage): AWS (Multi-Region Deployments, Cloud formation) 

·        Azure DevOps (TFS) - JIRA

·        Good presentation skills & communications skills

Written & verbal skills especially to confidently express technical ideas / solutions

2. AI Solution Architect.

Work experience: 10+ years

Location: Mumbai, Pune and Chennai.

Responsibilities: -

· Define and implement best practices for building, testing, and deploying scalable AI solutions, with a focus on generative models and LLMs using hyperscaler provided models or open-source models

· Infrastructure Management: Maintain the infrastructure required for machine learning operations, including cloud services, data storage, and computing resources

· Deployment and Automation: Develop and implement automation tools and frameworks for deploying machine learning models into production environments efficiently and reliably

· Model Versioning and Management: Establish processes for version control and management of machine learning models, ensuring traceability, reproducibility, and proper documentation

· Monitoring and Maintenance: Create monitoring systems to track model performance health, and data quality in production. Implement strategies for retraining and updating models as needed

· Collaboration with Data Scientists and Engineers: Collaborate with data scientists, machine learning engineers, and software developers to streamline the integration of models into applications, ensuring compatibility and scalability.

· Optimization and Scalability: Optimize machine learning workflows, algorithms, and infrastructure to improve performance, reduce latency, and handle scalability challenges.

· Continuous Integration/Continuous Deployment (CI/CD): Implement CI/CD pipelines specific to machine learning workflows to automate testing, integration, and deployment processes.

· Troubleshooting and Issue Resolution: Identify and address issues related to models, data pipelines, or infrastructure promptly to minimize downtime and maintain system reliability

· Documentation and Knowledge Sharing: Document processes, configurations, and best practices to facilitate knowledge sharing among team members and enable smooth onboarding of new team members

· Stay Updated with Emerging Technologies: Keep up to date with the latest advancements in AI/ML technologies, and methodologies to continuously improve operational efficiency.

· Cross-functional Communication: Collaborate with various teams across the organization, including data science, software engineering, operations, and business stakeholders, to align AI/ML activities with business objectives

Mandatory Technical skills

· Experience working in an AI / ML context alongside Data Scientists or ML Engineers

· Must have an in-depth understanding of generative models for text and image generations

· Proficiency in programming languages such as Python, R, and frameworks like TensorFlow or PyTorch

· Proficient in programming languages relevant to Azure and AI integration, such as Python, . NET, Java, or JavaScript

· Data Management and Analysis: Experience in handling large datasets, implementing data storage solutions, and performing data analysis.

· Strong leadership skills with experience in leading technical teams.

· Excellent problem-solving, analytical, and communication skills.

Good to have skills: -

· Prior experience in healthcare domain

· Certifications in AWS, GCP or Azure.

· Certifications in Data Science

How to Apply:-

If you're passionate about MLOps and AI and eager to join a dynamic team at the forefront of AI innovation, we'd love to hear from you.

To apply for this position, please send your updated resume highlighting your relevant skills and experience to or on LinkedIn itself.

Tag someone who might be interested in this role or share this post with your network.