Interesting Job Opportunity: Senior Software Engineer - Artificial Intelligence/Machine Learning

Techno-Comp Computer Services Pvt. Ltd.; Kolkata District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Techno-Comp Computer Services Pvt. Ltd. is seeking an AI Engineer to develop and maintain microservice architectures, API management, and data pipelines to support AI/ML model deployment.

You will collaborate with data scientists and product managers to acquire and preprocess data for model integration and optimization. You will design scalable LLM inference architectures, employ GPU memory optimization, quantization, and engage in prompt engineering.

Qualifications

• Experience deploying AI/ML models in production environments.
• Strong understanding of microservice architectures and API management.
• Familiarity with CI/CD tooling and container orchestration.

Responsibilities

• Develop and maintain microservice and API management for AI solutions.
• Collaborate with data scientists and product managers to acquire and preprocess data for model integration.
• Design scalable data pipelines to support AI/ML model training and inference.
• Debug and optimize machine learning models for performance and reliability.
• Implement infrastructure as code with Terraform, CloudFormation or Pulumi.
• Create CI/CD pipelines using GitHub Actions or Jenkins for streamlined workflows.
• Manage Kubernetes deployments and advanced service mesh configurations.
• Design scalable LLM inference architectures with GPU memory optimization and quantization.
• Engage in prompt engineering and fine-tuning for NLP models.
• Document architectures, experiments, and results with MLflow or DVC.

Skills

APIs design

Data orchestration

Cross-functional teamwork

Problem solving

Tools

Terraform

CloudFormation

Pulumi

GitHub Actions

Jenkins

Kubernetes

MLflow

DVC

Git

Job description

Description

• Develop and maintain microservice architecture and API management solutions using REST and gRPC for seamless deployment of AI solutions.
• Collaborate with cross-functional teams, including data scientists and product managers, to acquire, process, and manage data for AI/ML model integration and optimization.
• Design and implement robust, scalable, and enterprise-grade data pipelines to support state-of-the-art AI/ML models.
• Debug, optimize, and enhance machine learning models, ensuring quality assurance and performance improvements.
• Familiarity with tools like Terraform, CloudFormation, and Pulumi for efficient infrastructure management.
• Create and manage CI/CD pipelines using Git-based platforms (e.g., GitHub Actions, Jenkins) to ensure streamlined development workflows.
• Operate container orchestration platforms like Kubernetes, with advanced configurations and service mesh implementations, for scalable ML workload deployments.
• Design and build scalable LLM inference architectures, employing GPU memory optimization techniques and model quantization for efficient deployment.
• Engage in advanced prompt engineering and fine-tuning of large language models (LLMs), focusing on semantic retrieval and chatbot development.
• Document model architectures, hyperparameter optimization experiments, and validation results using version control and experiment tracking tools like MLflow or DVC.
• Research and implement cutting-edge LLM optimization techniques, such as quantization and knowledge distillation, ensuring efficient model performance and reduced computational costs.
• Collaborate closely with stakeholders to develop innovative and effective natural language processing solutions, specializing in text classification, sentiment analysis, and topic modeling.
• Design and execute rigorous A/B tests for machine learning models, analyzing results to drive strategic improvements and decisions.
• Stay up-to-date with industry trends and advancements in AI technologies, integrating new methodologies and frameworks to continually enhance the AI engineering function.
• Contribute to creating specialized AI solutions in healthcare, leveraging domain-specific knowledge for task adaptation and deployment.