Sr AI/ML Engineer with ML Ops-- W2

We are looking for an AI/ML Engineer with a minimum of 6+ years of experience. Your experience

and skills in Python, SQL, ML & Ops Engineering, and MLOps using AWS Cloud and Kubernetes,

along with your ability to lead and innovate in deploying ML/AI pipelines, reflect a strong foundation

in the field. Your focus on scalable architecture, collaboration across teams, and continuous learning

in ML research positions you well to tackle complex business problems and deliver impactful

solutions.

Please include LinkedIn URL in the resume, resumes with no LinkedIn URL will be rejected.

Required Qualifications:

• Ability to adapt to new technologies, tools, and methods in the rapidly evolving
• MLOps landscape.
• Constant drive to innovate and apply the latest ML research to improve
• processes and solve complex problems.
• Proficiency in Python and SQL for scripting, data manipulation, and
• programming, using tools like Jupyter Notebooks and Pandas.
• Experience with ML frameworks such as TensorFlow, PyTorch, and Scikit-
• Learn, for model development and training.
• Hands-on experience with cloud platforms like AWS (SageMaker, Lambda),
• Azure (ML Studio), or Google Cloud (AI Platform) for deploying and scaling ML
•  
• Knowledge of containerization using Docker and orchestration with
• Kubernetes (K8s) for scalable and automated model deployments.
• Familiarity with CI/CD pipelines using tools like Jenkins, GitLab CI/CD, or
• Circle CI for automating the deployment and integration of ML models.
• Expertise in data engineering and pipeline creation using technologies like
• Apache Spark, Apache Airflow, or Kafka for data processing and workflow
•  
• Strong understanding of model monitoring and logging using tools like
• Prometheus, Grafana, or the ELK stack (Elasticsearch, Logstash, Kibana) to
• ensure model performance in production.
• Experience with version control using Git/GitHub and model versioning tools
• like MLflow, DVC, or TFX (TensorFlow Extended).
• Ability to collaborate with cross-functional teams using Agile methodologies and tools like Jira, Slack, or Confluence, aligning ML initiatives with business
• Strong problem-solving and analytical skills for model evaluation,
• optimization, and addressing deployment challenges, using techniques like A/B
• testing, hyperparameter tuning, and statistical analysis.

Job Responsibilities:

• Job responsibilities will change dynamically as ML Ops space evolves and platform needs
• Some of the job responsibilities listed below for reference.
• Design, build, and maintain scalable ML infrastructure using cloud platforms like AWS,
• Azure, or Google Cloud, ensuring efficient model deployment and management.
• Develop and implement CI/CD pipelines using tools like Jenkins, GitLab CI/CD, or CircleCI,
• to automate the integration and deployment of machine learning models.
• Containerize ML applications using Docker and orchestrate deployments with Kubernetes
• (K8s) to ensure scalability, reliability, and seamless updates.
• Collaborate with data scientists, data engineers, and software engineers to integrate ML
• models into production systems, ensuring alignment with business objectives.
• Monitor and maintain the performance of ML models in production using monitoring tools
• like Prometheus, Grafana, or ELK stack, and implement alerts for model drift or performance
•  
• Manage the versioning and lifecycle of ML models using tools like MLflow, DVC, or TFX,
• ensuring reproducibility, traceability, and compliance.
• Optimize ML pipelines for performance and cost-efficiency, employing techniques such as
• distributed computing, data parallelism, and automated hyperparameter tuning.
• Ensure the security and compliance of ML models and data pipelines by implementing best
• practices in data encryption, access controls, and audit logging.
• Conduct experiments and A/B tests to evaluate model performance and iterate on models
• based on feedback, evolving requirements, and new data.
• Provide technical leadership and mentorship to a team of ML engineers, fostering a
• collaborative environment and driving continuous improvement in MLOps practices.

Education:

• Required: Bachelor s degree in computer science, Engineering, Statistics, Physics, Math, or

related field or equivalent experience