About the job
Join our team as a Senior Machine Learning Engineer and drive the development of cutting-edge ML and GenAI solutions. Collaborate with top engineers, design scalable systems, and deploy impactful AI features. Shape the future of intelligent automation and make a real-world impact in a dynamic, innovative environment. Grow your career with us!
Responsibilities
Bring a 0→1 product mindset, helping shape ideas into real, measurable impact.
Design, build, and optimize backend services that power ML and Generative AI features.
Develop, evaluate, and deploy ML models using classical, deep learning, and GenAI approaches.
Contribute to agentic systems and orchestration frameworks that enable intelligent, multi-step reasoning and task automation.
Collaborate with cross-functional teams to integrate ML solutions into production workflows.
Analyze and improve the efficiency, accuracy, and scalability of AI-enabled systems.
Stay up to date with advancements in ML, GenAI, and prompt optimization research.
Mentor junior engineers and help grow the team’s technical depth.
Qualifications
Minimum
Master’s or Ph.D. in Computer Science, Machine Learning, or a related technical field.
5+ years of experience in machine learning engineering, applied research, or production ML systems.
Strong Python software engineering skills, including system design, clean architecture, testing, CI/CD, version control, and code review best practices.
Experience taking ML-powered features from 0→1 through production and ongoing iteration.
Hands-on experience deploying and monitoring ML models in production environments.
Experience designing or contributing to agentic architectures and multi-agent orchestration systems.
Strong understanding of classical ML, deep learning, and modern Generative AI techniques.
Familiarity with cloud platforms (AWS, GCP, or Azure) for scalable ML deployment.
Solid foundation in data structures, algorithms, and distributed system design.
Comfortable leveraging AI coding agents to accelerate development workflows.
Excellent communication skills and demonstrated technical leadership experience.
Preferred
Experience with Generative AI systems, prompt optimization frameworks, and LLM-as-a-judge / evaluation methodologies.
Deep understanding of Retrieval-Augmented Generation (RAG) and modern NLP pipelines.
Experience with MLOps tooling, experiment tracking, model lifecycle management, and observability frameworks.
Track record of mentoring engineers and influencing technical direction on high-visibility projects.