We are seeking a highly motivated and skilled Senior Associate Software Engineer to join our AI Platform team. This role is ideal for someone passionate about building scalable, reliable, and high-performance AI infrastructure and services. You will work closely with machine learning engineers, data scientists, and platform architects to design and implement core components of our AI/ML platform, enabling seamless development, deployment, and monitoring of AI models across the enterprise.

What you will do

• Design, develop, and maintain core components of the AI/ML platform including model training pipelines, deployment services, and monitoring tools.
• Collaborate with ML engineers and data scientists to understand requirements and translate them into scalable platform features.
• Implement automation for model lifecycle management including versioning, testing, deployment, and rollback.
• Develop reusable libraries and APIs to support AI/ML workflows and integrations.
• Ensure platform reliability, scalability, and performance through rigorous testing and monitoring.
• Support integration of security, compliance, and governance features into the AI platform.
• Participate in sprint planning, provide technical estimates, and contribute to architectural decisions.
• Continuously improve platform capabilities by evaluating and integrating emerging technologies.

What we expect of you

• Bachelor’s degree in Computer Science, Engineering, or related field.
• 5–9 years of experience in software engineering, preferably in AI/ML platform development.
• Certifications in cloud platforms or AI/ML technologies are a plus (e.g., AWS AI, Azure AI Engineer, Google Cloud ML).

Must-Have Skills

• Strong programming skills in Python, or Java.
• Experience building distributed systems and microservices.
• Familiarity with MLOps tools and frameworks (e.g., MLflow, Kubeflow, Airflow).
• Experience with containerization and orchestration (Docker, Kubernetes).
• Solid understanding of CI/CD pipelines and DevOps practices.
• Experience with cloud platforms (AWS, GCP, Azure) and cloud-native services.
• Understanding AI/ML lifecycle and model deployment best practices.
• Ability to learn quickly, be organized and detail oriented.
• Experience with big data tools (Spark, Hadoop) and data pipeline development.
• Familiarity with model monitoring and observability tools.
• Knowledge of security principles and compliance in AI systems.
• Experience with infrastructure-as-code tools (Terraform, CloudFormation).
• Exposure to GenAI technologies and frameworks (e.g., LangChain, Vector DBs).

Compensation

5–9