Machine Learning Engineer

An ML engineer at IFS is part software engineer, part dev ops engineer and ML savvy, who combines knowledge of systems and applications, machine learning and artificial intelligence to build and deploy AI-driven solutions. The work is focused on designing and maintaining high-performance, scalable AI/ML infrastructure, building, and executing ML pipelines efficiently, serving models at scale, and creating the tools for continuous monitoring and improvements. Someone in this role uses their technical know-how to translate high-value and innovative AI opportunities into deployable and sustainable products. ML engineers are well-versed on AI/ML techniques, ranging from statistical learning, computer vision to generative AI. They have strong foundations in software engineering, ML algorithms and mastered several AI/ML serving frameworks. ML engineers work in close collaboration with data scientists, data engineers, architects, and DevOps engineers, on the creation and deployment of highly scalable infrastructure and AI-driven solutions. ML engineers act as AI infrastructure experts in the development of production-ready, scalable ML pipelines and AI-driven solutions along with our data scientist, data engineers, domain experts, partners, and clients. In addition, ML engineers at IFS continuously expand their knowledge of AI infrastructure, model monitoring/observability and drift detection, functional and business process domain knowledge and share this knowledge to guide others. Required Skills: * Programming Skills: Strong proficiency in Python and experience with relevant libraries (NumPy, Pandas, Kserve, etc.). Knowledge of additional languages like Go, C#, or SQL is a plus. * Cloud Platforms & Dev Ops: Familiarity with cloud computing platforms (Azure), infrastructure as code (terraform, helm charts), CI/CD, Git Ops with ArgoCD and containerization technologies (Docker, Kubernetes). * AI/ML Fundamentals: Foundational understanding of machine learning concepts, including supervised and unsupervised learning, deep learning, and model evaluation techniques. * Data Handling: Proficiency in data preprocessing and feature engineering. Experience with relational and vector databases. * Analytical Skills: Excellent analytical skills, with the ability to interpret complex datasets and derive actionable insights. * Communication Skills: Excellent verbal and written communication skills, with the ability to effectively collaborate with cross-functional teams and stakeholders * Education and Experience: A bachelor's or master's degree in Computer Science, Software Engineering, Data Science, or a related field, with at least 3+ years of experience in software development, including some experience with AI/ML frameworks such as TensorFlow, PyTorch, or scikit-learn.