Software Consultant

Functional/ Technical Design, develop and deploy machine learning solutions and services Implement end-to-end machine learning pipelines from data ingestion to training and model serving Operationalize LLMs, embeddings, and multi-agent systems in real-world applications Manage the machine learning and model lifecycle (experimentation, registry, deployment) Oversee the model promotion lifecycle, coordinating validation gates and approval workflows to safely deploy new model versions from stating to production Containerize applications using Docker and orchestrate them via Kubernetes Build and maintain CI/CD pipelines for ML models and LLM applications Collaborate with data scientists to refactor research code into production-ready Python code Monitor model performance, data drift, and performance in production Assess and integrate AI solutions ensuring optimal performance and reliability Design and implement production grade RAG systems Collaborate with infrastructure teams, data engineers, data scientists, and other stakeholders to integrate machine learning solutions into existing systems and processes Participate in code reviews, testing, and debugging to ensure the quality and reliability of machine learning solutions Competencies Strong problem-solving and analytical skills, with the ability to think critically and creatively about complex challenges Excellent communication and collaboration skills, with the ability to work effectively with cross-functional teams and stakeholders at all levels of the organization Ability to manage personal workloads effectively, to prioritize tasks, manage timelines, and deliver high-quality results on schedule Continuous learning mindset, with a passion for staying up to date with the latest advancements in machine learning and artificial intelligence Attention to detail and commitment to producing high-quality, reliable, and maintainable code Education and skills requirements Bachelor's or Master's degree in Data Science, Computer Science, Mathematics, Statistics, or a related field Advanced proficiency in Python programming with a focus on writing clean, testable and efficient code DevOps & Containers: Proficient with Docker for containerization and working knowledge of Kubernetes (k8s) for orchestration Practical understanding of GPU architecture and cloud compute instances to optimize resource allocation for training and inference workloads MLOPS tools: hands on experience with MLflow (or similar tools like weights & biases) for experiment tracking and model registry Proven experience working with Large Language Models (LLMs) Good understanding of AI agents & agentic workflows, LLM orchestration frameworks and reasoning patterns Experience with data preprocessing, feature engineering, and model selection and evaluation techniques Hands-on experience with CI/CD pipelines (GitLab, Jenkins) Knowledge of statistical and mathematical concepts relevant to machine learning, such as probability, lin