Data Scientist - Artificial Intelligence/Machine Learning
First seen by Alion on Oct 29, 2026.
Description :
JOB RESPONSIBILITY :
- 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.
- 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.
- 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 :
- Specialized training, certifications, and/or other special requirements: Nice to have
- Preferred education: Computer Science/Engineering.
EXPERIENCE : Minimum relevant experience - 4+ years in AI Engineering
SKILLS AND COMPETENCIES :
Technical Skills :
- 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 :
- 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
Skills
Data Scientist, Data Science, Artificial Intelligence, Machine Learning, LLM, GPU, Python, Tensorflow, MLOps