Sr. Associate - Machine Learning Engineer

Overview

Design, build and operationalize generative and agentic AI systems that are safe, auditable and production ready. Own LLM and agent capabilities, evaluation frameworks and guardrails while moving models from prototype into trusted enterprise services.

What you'll do

• Design, implement and deploy LLM solutions and agent frameworks for production workflows, focusing on reliability, interpretability and safety.
• Build and run evaluation pipelines with automated metrics, unit and integration test suites, adversarial/red-team tests, and human-in-the-loop evaluation.
• Create and operationalize guardrails, including prompt patterns, policy enforcement, input sanitization, output validation, data filtering and explainability tools.
• Prototype and productionize agentic behaviours, including RAGs, multi-step planners, tool-use interfaces, state management and safe action execution.
• Implement MLOps best practices, including model versioning, CI/CD for models, scalable serving, observability and cost controls.
• Partner with security, privacy, regulatory and ethics teams to embed compliance and auditability into deployments.
• Stay updated with the latest trends and advancements.
• Drive projects from design through sustained operations.
• Align product, platform, governance and data science stakeholders.
• Keep pace with LLM and agent research and apply best practices.

What you'll need

• 5 to 9 years of applied machine learning or software engineering experience, including at least 2 years on production ML systems.
• B.Tech, M.Tech or MS with specialization in Computer Science.
• Strong hands-on experience with large language models, including fine-tuning, instruction tuning, retrieval augmentation and embeddings.
• Practical experience with agent frameworks and multi-step agent behaviours.
• Solid software engineering skills in Python and deep learning frameworks such as PyTorch, JAX or TensorFlow.
• Experience building evaluation suites, human-in-the-loop workflows and adversarial testing.
• Demonstrated implementation of guardrails and safety mechanisms for NLP/LLM systems.
• Experience deploying services in cloud environments and with container orchestration (Kubernetes) and model serving technologies.
• Clear communication skills to translate prototypes into production solutions and engage cross-functional stakeholders.

Nice to have

• Prior work on Agentic AI, multi-agent systems, planners or applied agent safety research.
• Experience with benchmarking and continuous evaluation tooling.
• Background in regulated industries such as pharma or healthcare.
• Familiarity with Traditional ML, data governance and model risk management.
• Advanced technical credentialing or proven contributions to open-source or academic work in LLMs or agent systems.

Details

• Location: Hyderabad, India.
• Level 4.