Senior/Staff Engineer

Design, develop, and deploy machine learning models for predictive, prescriptive, and generative tasks using advanced statistical, ML, Deep Learning and analytical techniques. Architect and lead the end-to-end development of ML & Agentic workflows from concept to production. Build and operationalize agentic workflows capable of autonomous decision-making and adaptive learning, using frameworks like LangGraph, LangChain, CrewAI, and AutoGen. Define and implement production-grade agentic architectures with modularity, observability, and fault tolerance to ensure reliability and maintainability. Proven hands-on experience with cloud platforms, particularly Google Cloud Platform (GCP), is highly advantageous. Familiarity with cloud-native tools and infrastructure for model training, orchestration, and monitoring will significantly enhance your effectiveness and impact in this role. Establish and promote Best Known Methods (BKMs) for deploying ML & agentic solutions, including prompt management, memory handling, and agent orchestration strategies, Error handling and auto-updating of models. Continuously evaluate and fine-tune model and agent performance using structured evaluation frameworks, RLHF, prompt tuning, and feedback loops. Extract, cleanse, and analyze data from diverse sources using SQL and other query languages, applying techniques for outlier detection and missing data handling. Collaborate with cross-functional teams to integrate ML and agentic solutions into scalable systems aligned with business goals and user needs. Mentor junior engineers, conduct code reviews, and contribute to technical design and architectural decisions. Lead the selection and implementation of ML and agentic tools, frameworks, and infrastructure for efficient development and deployment. Stay current with emerging trends in machine learning, agentic AI, and related technologies to drive innovation and continuous improvement. Strong software development skills. Strong verbal and written communication skills. Programming Languages: Python (preferred), TensorFlow, PyTorch, Scikit-learn, JAX Agentic AI Frameworks: LangChain, LangGraph, AutoGen, CrewAI, Google ADK Machine Learning Techniques: Supervised, unsupervised, reinforcement learning, deep learning, generative models Data Engineering & Analysis: SQL, PySpark, SparkR, SparklyR; outlier detection, missing data handling Visualization & Reporting: Tableau or similar tools Cloud Platforms: Experience with Google Cloud Platform (GCP) or other cloud environments for scalable ML deployment. ML Ops & Infrastructure: MLFlow, Ray, Hugging Face, OpenAI Gym Evaluation & Optimization: RLHF, prompt tuning, structured evaluation frameworks 3-7 years of experience in data science, machine learning, or AI engineering roles Proven track record of working on ML projects end to end and have experience in deploying ML models and agentic workflows in production Prior contributions to technical design, architecture decisions, and mentoring junior engineers Publications in top-tier conferences (CVPR, NIPS, ICML, KDD) are a plus, though not required Strong analytical thinking and problem-solving abilities Excellent communication and collaboration skills