What are we looking for? We're seeking an experienced DevOps Engineer to join our engineering team and help design, build, and scale secure, high-performance infrastructure for our Cognitive Automation product. This role is not just about managing pipelines and cloud platforms-it's about architecting resilient systems, driving automation, and enabling seamless deployments across global financial firms. We're looking for self-driven, energetic professionals eager to innovate in a fast-paced, domain-tech environment. Responsibilities Design, build, and maintain scalable CI/CD pipelines using tools like Jenkins, GitLab CI, or GitHub Actions. Manage and optimize cloud infrastructure on AWS and Azure. Automate infrastructure provisioning using Infrastructure as Code (IaC) tools such as Terraform or CloudFormation. Implement and manage containerization with Docker and orchestration with Kubernetes. Monitor system performance, reliability, and security using tools like Prometheus, Grafana, ELK, or similar. Collaborate with development, QA, and product teams to streamline development-to-deployment workflows. Perform root cause analysis for production errors and implement fixes and improvements. Design and implement cost-effective infrastructure solutions that optimize performance, scalability, and resource utilization across cloud and hybrid environments. Ensure High Availability (HA) and fault tolerance in all infrastructure components to meet business continuity and uptime requirements. Build and manage secure infrastructure in line with financial industry regulations (e.g., PCI-DSS, SOC 2, ISO 27001), applying security-by-design principles across the DevOps lifecycle. Proven experience in migrating infrastructure and workloads from On-Premise to Cloud and Cloud to On-Prem, with deep understanding of hybrid cloud architecture, network connectivity, and data transfer strategies. Hands-on familiarity with most major cloud services and features in AWS and Azure, including but not limited to compute, storage, networking, IAM, monitoring, autoscaling, disaster recovery, and cost management tools. Requisites Bachelor's degree in computer science, Engineering, or related field (or equivalent experience). 5+ years of experience in a DevOps or Site Reliability Engineering (SRE) role. Hands-on experience with AWS and Azure cloud platforms (compute, storage, networking, IAM). Strong knowledge of CI/CD tools (e.g., Jenkins, GitLab CI/CD, GitHub Actions, Azure DevOps). Experience with Docker, Kubernetes, and container orchestration. Proficiency in scripting languages such as Bash, Python, or PowerShell. Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack, CloudWatch). Solid understanding of networking concepts, load balancing, and DNS. Experience in managing hybrid or multi-cloud environments. Familiarity with SIEM tools, vaulting solutions (e.g., HashiCorp Vault), and network security. Knowledge of financial data security standards: PCI-DSS, GDPR, SOX, etc. Hands on experience in writing deployment pipeline files/script. Good To Have Certifications like AWS Certified DevOps Engineer, Azure DevOps Engineer Expert, etc. Experience working in financial services, banking, or fintech environments. Familiarity with GitOps principles and tools like ArgoCD or Flux. Familiarity with AI/ML services and integrations offered by major cloud providers (e.g., AWS SageMaker, Azure ML, Vertex AI) and the ability to support infrastructure for data science and machine learning workflows. Experience with Google Cloud Platform (GCP) services such as Compute Engine, Cloud Functions, BigQuery, and GKE. Exposure to multi-cloud or cloud-agnostic architectures, ensuring portability and resilience across AWS, Azure, and GCP. Basic understanding of MLOps practices, pipelines, and model deployment lifecycle. (ref:hirist.tech)
Academic / Professional Qualifications Bachelor's or Masters degree in Computer Science, Mathematics, Machine Learning or a related field. A Ph.D. in any of the above fields would be a big Exposure / Experience : Experience in building NLP models, from ground up and a solid grasp of vectorization, tokenization, encoders/decoder setup, backpropagation and bi-directional Objective : You would be responsible for developing, implementing, and optimizing state-of-the-art NLP algorithms and models, right from conception to production. You will deploy models into production systems, ensuring scalability and reliability. Your responsibilities will include, among other things, the following : Develop and implement machine learning models and NLP algorithms to solve real-life business problems and improve system stability and performance. Collaborate with product leads, business analysts and clients to understand product requirements, define objectives, and design appropriate solutions. Collect, pre-process, and analyze large datasets to extract relevant features and insights for model development. Apply various machine learning techniques, such as deep learning, reinforcement learning, supervised and unsupervised training to develop robust and scalable models. Optimize and fine-tune the models to improve accuracy, efficiency, and scalability. Develop data pipelines and infrastructure for efficient data ingestion, storage, and retrieval. Stay up to date with the latest advancements in NLP, machine learning and related fields, and incorporate relevant techniques into the existing products as well as those under development. Work closely with software engineers to integrate machine learning models into production systems and ensure scalability and reliability (ref:hirist.tech)