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.
Qualification
Minimum education : Bachelors degree in any Engineering Stream
Specialized training, certifications, and/or other special requirements : Nice to have
Preferred education : Computer :
Minimum relevant experience - 6+ years in AI Engineering
Skills And Competencies
Technical Skills :
Advanced proficiency in Python with expertise in data science libraries (NumPy, Pandas, scikit-learn) and deep learning frameworks (PyTorch, TensorFlow)
Extensive experience with LLM frameworks (Hugging Face Transformers, LangChain) and prompt engineering techniques
Experience with big data processing using Spark for large-scale data analytics
Version control and experiment tracking using Git and MLflow
Software Engineering & Development : Advanced proficiency in Python, familiarity with Go or Rust, expertise in microservices, test-driven development, and concurrency processing.
DevOps & Infrastructure : Experience with Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes) with Helm and service mesh implementations.
LLM Infrastructure & Deployment : Proficiency in LLM serving platforms such as vLLM and FastAPI, model quantization techniques, and vector database management.
MLOps & Deployment : Utilization of containerization strategies for ML workloads, experience with model serving tools like TorchServe or TF Serving, and automated model retraining.
Cloud & Infrastructure : Strong grasp of advanced cloud services (AWS, GCP, Azure) and network security for ML systems.
LLM Project Experience : Expertise in developing chatbots, recommendation systems, translation services, and optimizing LLMs for performance and security.
General Skills : Python, SQL, knowledge of machine learning frameworks (Hugging Face, TensorFlow, PyTorch), and experience with cloud platforms like AWS or GCP.
Experience in creating LLD for the provided architecture.
Experience working in microservices based architecture.
Domain Expertise
Strong mathematical foundation in statistics, probability, linear algebra, and optimization
Deep understanding of ML and LLM development lifecycle, including fine-tuning and evaluation
Expertise in feature engineering, embedding optimization, and dimensionality reduction
Advanced knowledge of A/B testing, experimental design, and statistical hypothesis testing
Experience with RAG systems, vector databases, and semantic search implementation
Proficiency in LLM optimization techniques including quantization and knowledge distillation
Understanding of MLOps practices for model deployment and monitoring
Professional Competencies
Strong analytical thinking with ability to solve complex ML challenges
Excellent communication skills for presenting technical findings to diverse audiences
Experience translating business requirements into data science solutions
Project management skills for coordinating ML experiments and deployments
Strong collaboration abilities for working with cross-functional teams
Dedication to staying current with latest ML research and best practices
Ability to mentor and share knowledge with team members
