**Description:** Model Deployment and Operations: Deploy, monitor, and maintain machine learning models in production environments. Automate model training, retraining, versioning, and governance processes. Monitor model performance, detect drift, and ensure scalability and reliability of ML workflows Infrastructure and Pipeline Management: Design and implement scalable MLOps pipelines for data ingestion, transformation, and model deployment. Build infrastructure-as-code solutions using tools like Terraform to manage cloud environments (AWS, GCP) Collaboration with Teams: Work closely with data scientists to operationalize machine learning models. Collaborate with software engineers to integrate ML systems into broader platforms Cloud and Big Data Expertise: Utilize cloud services from AWS, GCP, and Snowflake for scalable data storage and processing. DevOps Integration: Implement CI/CD pipelines and automations to streamline ML model deployment. Use containerization tools like Docker and orchestration platforms like Kubernetes for scalable deployments Use Observability platforms to monitor pipeline and operational health of model production, delivery and execution **Requirements:** Technical Skills: Proficiency in Python for ML development; familiarity with additional languages like Clojure is a plus. Expertise in cloud platforms (AWS, GCP) and data warehouses like Snowflake or BigQuery. Strong knowledge of MLOps frameworks (e.g., Kubeflow, MLflow) and DevOps tools (e.g., Jenkins, GitLab, Flux) Experience with containerization (Docker) and orchestration (Kubernetes) Experience with infrastructure-as-code tools like Terraform Machine Learning Knowledge: Solid understanding of machine learning principles, including model evaluation, explainability, and retraining workflows. Hands-on experience with ML frameworks such as TensorFlow or PyTorch Big Data Handling: Proficiency in SQL/NoSQL databases and distributed computing systems like Dataprov, EMR, Spark, Hadoop Soft Skills: Strong communication skills to collaborate across multidisciplinary teams. Problem-solving mindset with the ability to work in agile environments Experience: At least 5+ years in platform, software, or MLOps engineering roles Proven track record of deploying scalable ML solutions in production environments **Job Responsibilities:** Model Deployment and Operations: Deploy, monitor, and maintain machine learning models in production environments. Automate model training, retraining, versioning, and governance processes. Monitor model performance, detect drift, and ensure scalability and reliability of ML workflows Infrastructure and Pipeline Management: Design and implement scalable MLOps pipelines for data ingestion, transformation, and model deployment. Build infrastructure-as-code solutions using tools like Terraform to manage cloud environments (AWS, GCP) Collaboration with Teams: Work closely with data scientists to operationalize machine learning models. Collaborate with software engineers to integrate ML systems into broader platforms Cloud and Big Data Expertise: Utilize cloud services from AWS, GCP, and Snowflake for scalable data storage and processing. DevOps Integration: Implement CI/CD pipelines and automations to streamline ML model deployment. Use containerization tools like Docker and orchestration platforms like Kubernetes for scalable deployments Use Observability platforms to monitor pipeline and operational health of model production, delivery and execution **What We Offer:** **Exciting Projects:** We focus on industries like High-Tech, communication, media, healthcare, retail and telecom. Our customer list is full of fantastic global brands and leaders who love what we build for them. **Collaborative Environment:** You Can expand your skills by collaborating with a diverse team of highly talented people in an open, laidback environment — or even abroad in one of our global centers or client facilities! **Work-Life Balance:** GlobalLogic prioritizes work-life balance, which is why we offer flexible work schedules, opportunities to work from home, and paid time off and holidays. **Professional Development:** Our dedicated Learning & Development team regularly organizes Communication skills training(GL Vantage, Toast Master),Stress Management program, professional certifications, and technical and soft skill trainings. **Excellent Benefits:** We provide our employees with competitive salaries, family medical insurance, Group Term Life Insurance, Group Personal Accident Insurance , NPS(National Pension Scheme ), Periodic health awareness program, extended maternity leave, annual performance bonuses, and referral bonuses. **Fun Perks:** We want you to love where you work, which is why we host sports events, cultural activities, offer food on subsidies rates, Corporate parties. Our vibrant offices also include dedicated GL Zones, rooftop decks and GL Club where you can drink coffee or tea with your colleagues over a game of table and offer discounts for popular stores and restaurants!