Senior ML Engineer

Responsibilities

• Design, develop, and optimize machine learning models for applications across different domains.
• Build natural language processing pipelines for tasks such as text generation, summarization, translation, and more.
• Develop and deploy cutting‑edge generative AI and Agentic AI applications.
• Implement MLOps practices: model training, evaluation, deployment, monitoring, and maintenance.
• Integrate machine learning capabilities into existing products or build new AI‑powered applications.
• Perform data mining, cleaning, preparation, and augmentation to train robust ML models.
• Collaborate with cross‑functional teams to translate AI requirements into technical implementations.
• Ensure model performance, scalability, and reliability.
• Continuously research and implement state‑of‑the‑art AI/ML algorithms and techniques.
• Manage the end‑to‑end ML lifecycle from data processing to production deployment.

Key Required Skills

• Minimum 7 years of experience in machine learning engineering and AI development.
• Deep expertise in machine learning algorithms and techniques such as supervised/unsupervised learning, deep learning, reinforcement learning, etc.
• Solid experience in natural language processing (NLP) – language models, text generation, sentiment analysis.
• Proven understanding of generative AI concepts: text, image, audio synthesis, diffusion models, transformers.
• Expertise in Agentic AI and building real‑world applications using the same.
• Experience with Agentic AI frameworks like Langgraph, ADK, Strands, Microsoft Agent Framework.
• Hands‑on experience developing and deploying generative AI applications (text generation, conversational AI, image synthesis).
• Experience in MLOps and ML model deployment pipelines.
• Proficiency in programming languages such as Python and ML frameworks like Tensor Flow, PyTorch.
• Knowledge of cloud platforms (AWS, GCP, Azure) and tools for scalable ML solution deployment.
• Experience with data processing, feature engineering, and model training on large datasets.
• Familiarity with responsible AI practices, AI ethics, model governance, and risk mitigation.
• Understanding of software engineering best practices and applying them to ML systems.
• Experience in an agile development environment.
• Exposure to tools/frameworks for monitoring and analyzing ML model performance and data accuracy.
• Strong problem‑solving, analytical, and communication abilities.
• Bachelor’s or master’s degree in computer science, AI, statistics, mathematics or related fields.