As a Senior Machine Learning Engineer, you will work closely with top notch engineers and data scientists to design, develop, evaluate and deploy Gen AI-powered solutions for scalable, customer-facing applications. Your work will focus on building and applying state-of-the-art agentic capabilities to drive business impact and improve efficiency.
Key Job Responsibilities and Duties:
Design, develop, and deploy high-quality, performant, and efficient Generative AI-powered solutions and agentic systems into production environments.
Evaluate and define optimal architectural solutions by considering emerging technologies, business needs, and technical requirements for latency, throughput, and scale.
Own services end-to-end, including implementing robust monitoring and maintenance strategies to ensure application and ML health, quality, and performance.
Write and maintain clean, scalable, and well-tested production code, ensuring reproducibility and seamless integration via CI/CD pipelines.
Pioneer and promote best practices and the adoption of cutting-edge technology in GenAI application development.
Collaborate effectively with Product Managers, Data Scientists, and Analysts to understand business requirements and translate them into technical ML and agentic solutions.
Provide technical guidance and mentorship to other engineers, contributing to the team's overall technical development.
Qualifications & Skills:
Bachelors or masters degree in Computer Science, Engineering, Statistics, or a related field.
Minimum of 6 years of experience as a Machine Learning Engineer or a similar role, with a consistent record of successfully delivering ML solutions to production.
Experience of working on products that impact a large customer base.
Demonstrable experience and capabilities with Generative AI applications, including Large Language Models (LLMs), Agentic Systems, and MCP in production environments. Experience deploying large-scale language models (e.g., GPT, BERT, or similar architectures) – an advantage.
Deep understanding of core machine learning algorithms, statistical models, evaluation methods, and data structures.
Experience in designing, building, and deploying models using cloud frameworks (e.g., AWS Sagemaker) and standard ML libraries (e.g., TensorFlow, PyTorch, or scikit-learn).
Strong programming proficiency in languages such as Python and Java.
Strong coding practices, including writing and reviewing production-quality, maintainable, and well-tested code, with the ability to effectively leverage modern AI coding assistants while maintaining high standards for correctness, readability, and system design.
Experience with big data processing frameworks (e.g., Pyspark, Apache Flink, Snowflake) and demonstrable experience with relational/NoSQL database systems (e.g., MySQL, Cassandra, DynamoDB).
Excellent English communication and presentation skills, both written and verbal.
Proficiency in data manipulation, analysis, and visualization using tools like NumPy, pandas, and matplotlib – an advantage.
Experience with experimental design, A/B testing, and evaluation metrics for ML models – an advantage.
