ML Engineer

Responsibilities:

• Lead the design, development, and productionization of machine learning and AI-driven products.
• Own ML initiatives from conceptualisation through deployment and continuous improvement.
• Develop scalable, reusable, and maintainable ML service components with quality-first principles.
• Build and deploy ML, Deep Learning, Generative AI, and Agentic AI solutions.
• Design and expose ML services using FastAPI and RESTful APIs.
• Collaborate with business stakeholders to translate requirements into technical solutions.
• Ensure robustness, scalability, performance, and maintainability of ML applications.
• Lead and mentor team members, fostering technical growth and best practices.
• Work in an Agile, DevOps-driven environment with distributed teams.
• Quickly learn and adopt new technologies as required to deliver results.

Requirements:

• 3-5 years of experience in Machine Learning and data modelling.
• Strong experience in Machine Learning, Deep Learning, and Large Language Models (LLMs).
• Hands-on expertise with TensorFlow, PyTorch, and ML libraries.
• Proficiency in Generative AI and Agentic AI, including automation use cases.
• Experience with agentic AI platforms such as Glean and Agent Bricks.
• Experience in developing and productionizing ML applications.
• Mentor developers and write clean, well-documented, and testable code.
• Bachelor's degree in computer science or a related field (required).
• A master's degree is a plus.

Data Engineering and Analytics:

• 7+ years of experience in statistics and analytics on large-scale datasets.
• Strong SQL and data analysis skills, including SQL DW concepts.
• Experience with Spark ecosystem: PySpark, Databricks, Synapse Notebooks (1+ year hands-on).
• Proficiency in Azure Data Services: Azure Synapse, Azure Data Factory, Azure Data Lake, Azure security model.
• Experience building pipelines for full and incremental data loads from Azure and on-prem sources.
• Strong knowledge of data management, processing, cleaning, transformation, aggregation, and modelling.
• Working knowledge of Power BI reports and dashboards (AAS / PBI).

Systems, Cloud and DevOps:

• Strong programming skills in Python (Java is a plus).
• Experience with FastAPI, RESTful services, and microservices architecture.
• Proficiency with databases such as MySQL, PostgreSQL, and MongoDB.
• Experience deploying applications using Docker and Kubernetes.
• Experience with streaming and messaging systems: Kafka, Kinesis, RabbitMQ.
• Familiarity with Azure Git and DevOps practices.

Leadership and Soft Skills:

• Proven experience leading projects end-to-end.
• Strong mentoring and team leadership experience.
• Excellent written and verbal communication skills.
• Ability to work effectively in distributed teams.
• Strong understanding of data structures and design patterns.
• Exposure to the retail industry is a plus.

Skills

ML, PyTorch, TensorFlow, deep learning, machine learning