Title: Sr AI/ML Engineer with ML Ops
Location: Remote
Duration: Long Term--W2
Job Description:
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