Associate AI/ML Engineer

About the Role

We are seeking an Associate AI/ML Engineer with strong software engineering fundamentals and growing depth in AI/ML and data platforms. This role emphasizes building production-ready, scalable AI services, applying Generative AI techniques, and continuously expanding expertise across machine learning and data engineering domains.

Responsibilities

• Design, implement, and optimize AI and machine learning solutions, including statistical models, deep learning, and Generative AI systems
• Execute proof-of-concepts, train models at scale, and baseline performance using quantitative evaluation metrics
• Build and operate large-scale training and inference pipelines using Databricks, PySpark, and cloud platforms (AWS, Azure, GCP)
• Apply RAG, LangChain, and Vector Databases to develop GenAI solutions
• Optimize and quantize models to improve performance, scalability, and cost efficiency
• Develop REST and FastAPI services, containerize solutions using Docker, and integrate UI tools such as Streamlit or Flask
• Partner with cross-functional teams to translate business needs into clear, scalable AI solutions, and present insights effectively
• Mentor engineers, participate in design and architecture reviews, and uphold standards for quality, safety, and trust

Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, or a related field
• 1+ years of professional software engineering experience, delivering high-quality, production-grade commercial applications end to end
• 1+ years of AI/ML engineering experience, including deploying models at scale and contributing to technical leadership across AI initiatives
• Demonstrated ability to design, build, deploy, and operate production-ready services, including CI/CD and cloud infrastructure
• Programming & Systems Expertise: 1+ years of hands-on experience with Java, Python, SQL, and scripting
• Cloud & Data Platform Experience: 1+ years of experience across AWS, Azure, and GCP, with deeper hands-on experience in AWS and cloud-native architectures
• Knowledge with Databricks, MongoDB, PySpark/SparkSQL, and data pipeline implementation
• Familiarity with Hadoop ecosystems and distributed data processing
• MLOps, Infrastructure & Governance: Experience with data governance concepts, including access control and platform-level controls in Databricks (Delta Lake, Unity Catalog)
• Working knowledge of MLOps practices, including model lifecycle management and operationalization
• Familiarity with Infrastructure as Code using Terraform and CloudFormation
• Technical Expertise: Solid knowledge of AI/ML frameworks, orchestration tools, and scalable architectures
• Proficiency with big data technologies, including Spark, Hadoop, and Kafka
• Solid foundation in data science principles, including statistics, probability theory, optimization, simulation, and data modeling