JOB RESPONSIBILITY :

- 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 Science/Engineering.

EXPERIENCE :

- 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