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Form of workJob Description
Job Title: Sr. level Machine Learning Operations Engineer (MLOps)
(MLOps) Engineer to help lead Mlops strategy and solutions. This role will help lead, implement and define the MLOps, LLMOps technology and platform strategy, and design and develop AI/Client/LLM/GAI technology platforms while working with a broad range of partners across data, technology and business teams.
In this role, you will play a pivotal role in helping lead and implementing our machine learning strategy and operations, ensuring the seamless deployment, monitoring, and management of our machine learning models and data pipelines. You will help lead Client infrastructure initiatives, mentor junior team members, and contribute to the strategic direction of our MLOps infrastructure. You will be instrumental in leading strategic direction for Client infrastructure in a world class AI Client team while working alongside well-known experts and researchers in AI Client modeling, Client engineers and data science and data engineering teams. You will contribute to setting roadmaps for AI operations and be a critical part of leading *** s AI-driven transformation to drive value internally and
for our customers.
*** is a leader in risk management solutions leveraging automation and AI/Client. This role is a unique opportunity for an experienced MLops / LLMops hands-on senior engineer to grow into the next step in their career journey and apply their domain expertise in deep learning, GenAI, and LLMs to drive business value for multiple stakeholders while mentoring and growing a Mlops team. The ideal candidate must have deep design and hands-on development expertise in Client, LLMs, MLOps, LLMOps, and integrating Client solutions with business functions to create the next generation of AI-powered capabilities.
Responsibilities include:
" MLOps Strategy: Develop and implement MLOps strategies, best practices, and standards to enhance AI Client model deployment and monitoring efficiency. Develop roadmap and strategy for MLOps and LLMOps Platforms and model lifecycle implementation
" Client Architecture Design and Development: Responsible for the design and development of
custom architecture for batch and stream processing-based AI Client pipelines including data
ingestion to preprocessing to scaled AI model compute and ensure the architecture meets all SLA requirements. Work closely with members of technology and business teams in the design, development, and implementation of Enterprise AI platform.
" Infrastructure Management: Oversee the design, deployment, and management of scalable and reliable infrastructure for AI, Client , GenAI, LLM model training and deployment.
" Model Deployment: Lead the deployment of GenAI, LLM , machine learning models into
production environments, ensuring reliability and scalability.
" Monitoring and Optimization: Create and maintain robust monitoring systems to track model performance, data quality, and infrastructure health. Identify and implement optimizations to improve system efficiency.
" Automation: Develop and maintain automated pipelines for model training, testing, and
deployment, optimizing for speed and reliability. Ensure CI-CD best practices are followed.
" Internal Collaboration: Collaborate closely with data scientists, machine learning engineers, and
software engineers to ensure smooth integration of machine learning models into production
systems.
" Stakeholder Engagement and Collaboration: Collaborate closely with business and PM
stakeholders in roadmap planning and implementation efforts and ensure technical milestones
align with business requirements.
" Security and Compliance: Implement security measures and compliance standards to protect
sensitive data and ensure adherence to industry regulations.
" Mentorship: Recruit, develop and mentor technical AI/Client engineering talent on the team
Provide guidance and mentorship to junior MLOps engineers, fostering their professional
growth and development.
" Documentation: Maintain comprehensive documentation of MLOps processes and procedures
for reference and knowledge sharing.
" Standards and Best Practices: Ensure the use of standards, governance and best practices in Client
pipeline monitoring and Client model monitoring, and adherence to model and data governance
standards
" Problem Solving: Troubleshoot complex issues related to machine learning model deployments
and data pipelines, and develop innovative solutions.
REQUIRED SKILLS/QUALIFICATIONS:
" Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
" Experienced professional (7+ years experience) as Client engineer, architect, engineer, lead data scientist in Big Data ecosystem or any similar distributed or public cloud platform, with a desire to assume greater responsibilities as a leader and mentor, while still being hands-on.
" 4+ years hands-on experience in integrating, evaluating, deploying, operationalizing Client and LLM
models at speed and scale, including integration with enterprise applications and APIs. (In
addition, ideal candidate should also have hands on experience on training and fine-tuning Client
and LLM models at scale)
" Expertise (4+ years) in distributed computing and orchestration technology (Kubernetes, Ray,
Airflow) and scaling
" Experience with public cloud platform & systems (AWS, GCP, Azure)
" Proficiency with Databricks, MLflow, Flink, GPT4All, Kore.ai, or similar AI/Client/Client Ops
technologies
" Experience developing with SQL, NoSQL, ElasticSearch, MongoDB, and Spark, Python, PySpark
for model development and Client Ops
" Knowledge of DevOps, MlOps principles and practices, and experience with version control
systems (e.g., Git) and CI/CD pipelines.
" Excellent written & verbal communication and stakeholder management skills
" Strategic thinker and influencer with demonstrated technical and business acumen and
problem-solving skills
" Experienced with LLMs (extractive and generative), fine-tuning and operationalizing LLM
pipelines. Strong familiarity with higher level trends in LLMs and open-source platforms
" Nice to have: Experience with contributing to Github and open source initiatives or in research projects
(MLOps) Engineer to help lead Mlops strategy and solutions. This role will help lead, implement and define the MLOps, LLMOps technology and platform strategy, and design and develop AI/Client/LLM/GAI technology platforms while working with a broad range of partners across data, technology and business teams.
In this role, you will play a pivotal role in helping lead and implementing our machine learning strategy and operations, ensuring the seamless deployment, monitoring, and management of our machine learning models and data pipelines. You will help lead Client infrastructure initiatives, mentor junior team members, and contribute to the strategic direction of our MLOps infrastructure. You will be instrumental in leading strategic direction for Client infrastructure in a world class AI Client team while working alongside well-known experts and researchers in AI Client modeling, Client engineers and data science and data engineering teams. You will contribute to setting roadmaps for AI operations and be a critical part of leading *** s AI-driven transformation to drive value internally and
for our customers.
*** is a leader in risk management solutions leveraging automation and AI/Client. This role is a unique opportunity for an experienced MLops / LLMops hands-on senior engineer to grow into the next step in their career journey and apply their domain expertise in deep learning, GenAI, and LLMs to drive business value for multiple stakeholders while mentoring and growing a Mlops team. The ideal candidate must have deep design and hands-on development expertise in Client, LLMs, MLOps, LLMOps, and integrating Client solutions with business functions to create the next generation of AI-powered capabilities.
Responsibilities include:
" MLOps Strategy: Develop and implement MLOps strategies, best practices, and standards to enhance AI Client model deployment and monitoring efficiency. Develop roadmap and strategy for MLOps and LLMOps Platforms and model lifecycle implementation
" Client Architecture Design and Development: Responsible for the design and development of
custom architecture for batch and stream processing-based AI Client pipelines including data
ingestion to preprocessing to scaled AI model compute and ensure the architecture meets all SLA requirements. Work closely with members of technology and business teams in the design, development, and implementation of Enterprise AI platform.
" Infrastructure Management: Oversee the design, deployment, and management of scalable and reliable infrastructure for AI, Client , GenAI, LLM model training and deployment.
" Model Deployment: Lead the deployment of GenAI, LLM , machine learning models into
production environments, ensuring reliability and scalability.
" Monitoring and Optimization: Create and maintain robust monitoring systems to track model performance, data quality, and infrastructure health. Identify and implement optimizations to improve system efficiency.
" Automation: Develop and maintain automated pipelines for model training, testing, and
deployment, optimizing for speed and reliability. Ensure CI-CD best practices are followed.
" Internal Collaboration: Collaborate closely with data scientists, machine learning engineers, and
software engineers to ensure smooth integration of machine learning models into production
systems.
" Stakeholder Engagement and Collaboration: Collaborate closely with business and PM
stakeholders in roadmap planning and implementation efforts and ensure technical milestones
align with business requirements.
" Security and Compliance: Implement security measures and compliance standards to protect
sensitive data and ensure adherence to industry regulations.
" Mentorship: Recruit, develop and mentor technical AI/Client engineering talent on the team
Provide guidance and mentorship to junior MLOps engineers, fostering their professional
growth and development.
" Documentation: Maintain comprehensive documentation of MLOps processes and procedures
for reference and knowledge sharing.
" Standards and Best Practices: Ensure the use of standards, governance and best practices in Client
pipeline monitoring and Client model monitoring, and adherence to model and data governance
standards
" Problem Solving: Troubleshoot complex issues related to machine learning model deployments
and data pipelines, and develop innovative solutions.
REQUIRED SKILLS/QUALIFICATIONS:
" Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
" Experienced professional (7+ years experience) as Client engineer, architect, engineer, lead data scientist in Big Data ecosystem or any similar distributed or public cloud platform, with a desire to assume greater responsibilities as a leader and mentor, while still being hands-on.
" 4+ years hands-on experience in integrating, evaluating, deploying, operationalizing Client and LLM
models at speed and scale, including integration with enterprise applications and APIs. (In
addition, ideal candidate should also have hands on experience on training and fine-tuning Client
and LLM models at scale)
" Expertise (4+ years) in distributed computing and orchestration technology (Kubernetes, Ray,
Airflow) and scaling
" Experience with public cloud platform & systems (AWS, GCP, Azure)
" Proficiency with Databricks, MLflow, Flink, GPT4All, Kore.ai, or similar AI/Client/Client Ops
technologies
" Experience developing with SQL, NoSQL, ElasticSearch, MongoDB, and Spark, Python, PySpark
for model development and Client Ops
" Knowledge of DevOps, MlOps principles and practices, and experience with version control
systems (e.g., Git) and CI/CD pipelines.
" Excellent written & verbal communication and stakeholder management skills
" Strategic thinker and influencer with demonstrated technical and business acumen and
problem-solving skills
" Experienced with LLMs (extractive and generative), fine-tuning and operationalizing LLM
pipelines. Strong familiarity with higher level trends in LLMs and open-source platforms
" Nice to have: Experience with contributing to Github and open source initiatives or in research projects
