Required Skills:

- MLOps
- Cloud Platform
- Python
- Kubernetes
- CI/CD
- Infrastructure as Code
- REST API Development

Nice to Have:

- GenAI / LLM
- Cloud Security
- Databricks

Senior ML Engineer Role Overview we are building a next generation enterprise AI Delivery team and are seeking an experienced, Lead ML Engineer with strong cloud engineering expertise (Azure/AWS), software engineering excellence, and hands-on experience in building scalable AI/ML platforms and services. You will focus on architecting, developing, deploying, and maintaining robust cloud-native AI systems, enabling ML/GenAI model integration, MLOps pipelines, API services, and production-grade infrastructure for enterprise AI products. The ideal candidate will have 3-5 years of experience, deep cloud engineering skills, and the ability to collaborate with data scientists, ML engineers, product teams, and business stakeholders to industrialize high-impact AI solutions. Key Responsibilities Design and implement scalable, secure, and cost‑efficient cloud solutions on AWS, ensuring high availability, monitoring, logging, and strong engineering quality. Develop and maintain cloud‑native applications, RESTful APIs, serverless components, microservices, and backend services supporting AI/ML use cases. Build and operate MLOps pipelines for model deployment, monitoring, lifecycle management, and integration of ML/GenAI models into production systems via APIs, containers, batch workflows, or event‑driven architectures. Work closely with Data Scientists to productionize models with scalable serving patterns, feature stores, CI/CD pipelines, and deployment best practices. Implement Infrastructure‑as‑Code (Terraform, ARM templates, etc.) and manage CI/CD workflows using Jenkins/Groovy, GitHub Actions, or equivalent tools. Develop containerized solutions (Docker) and orchestrate deployments using Kubernetes/AKS/EKS with strong focus on performance, reliability, and observability. Apply robust security controls,



identity/access management, and cloud governance practices aligned with enterprise compliance policies. Troubleshoot and resolve infrastructure issues, deployment failures, performance bottlenecks, and system reliability concerns across the cloud stack. Maintain architecture documentation, deployment guidelines, engineering playbooks, and reusable templates to standardize best practices. Collaborate with product, business, and engineering teams to deliver AI features; participate in design/code reviews and architecture forums; mentor junior engineers and contribute to engineering accelerators. Stay current with emerging cloud, AI engineering, and MLOps trends to drive continuous improvement and innovation. Required Skills & Qualifications Technical Experience: 3-5 years of hands-on experience designing and implementing cloud solutions on Azure (preferred) and/or AWS. Expertise in Python for backend services, automation, and API development. Strong understanding of cloud concepts: IaaS, PaaS, SaaS, serverless computing, and cloud-native architectures. Hands-on experience with: Azure App Services, Functions, App Insights, API Management AWS Lambda, ECS/EKS, S3, CloudWatch (if applicable) Terraform, ARM Templates, Ansible MLflow or equivalent ML lifecycle management tools Docker and Kubernetes / AKS Experience building CI/CD pipelines using Jenkins (Groovy scripts), Azure DevOps, or GitHub Actions. Deep knowledge of networking, security, identity, monitoring, and cost optimization in cloud environments.



Experience implementing scalable production-grade systems for AI/ML workloads is a strong plus. Consulting Experience: Proven track record in an IT consulting environment, engaging with large enterprises and MNCs in strategic data solutioning projects. Solid stakeholder management, business needs assessment, and change management skills. Leadership & Soft Skills: Experience managing and mentoring small teams, developing technical skills AI domains. Ability to influence and align cross-functional teams and stakeholders. Excellent communication, documentation, and presentation skills. Strong problem-solving, analytical thinking, and strategic vision. Educational Qualifications: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related quantitative field. Preferred Certifications: AWS Certified Machine Learning – Specialty AWS Certified Machine Learning Engineer – Associate (or equivalent in Azure) Databricks Machine Learning Professional or Databricks Generative AI Engineer is a plus Certified Artificial Intelligence Practitioner (CAIP) or similar GenAI/Responsible AI certifications Kubernetes Certifications (CKA/CKAD) for production deployment competency What We’re Looking For Self-starters who are highly motivated, ambitious, and eager to challenge the status quo. Builders who combine scientific rigor with pragmatic engineering and can balance accuracy, latency, and cost. Effective leaders who collaborate openly, freely share knowledge and elevate team performance. Straightforward, results-oriented individuals who value impact and accountability. Adaptable experts who stay on top of fast-evolving AI technologies and practices. Opportunity to shape and build an AI product portfolio that delivers meaningful business impact for Regions. Work alongside a motivated and innovative team that values learning, ownership, and excellence. Thrive in a culture that challenges the status quo and embraces diverse perspectives.

📌 Senior Machine Learning Engineer - A D (India)
🏢 Cigres Technologies
📍 India