AI Research Engineer

<p><p><strong>Job Role:</strong>- AI Research Engineer</p> <p><br></p> <p><strong>Job Location:</strong>- Bengaluru, India</p> <p><br></p> <p><strong>Experience:-</strong> 7+ Years</p> <p><br></p> <strong>Role Summary:-</strong> <p>We are seeking an AI Research Engineer to design, develop, and deploy scalable machine learning systems and AI-powered features. The role focuses on building LLM, computer vision, and multimodal machine learning pipelines, deploying models into production, and improving system reliability, performance, and cost efficiency.</p> <strong>Key Responsibilities:-</strong> <ul> <li>Design and develop AI features from data ingestion through real-time model inference.</li> <li>Build scalable, cost-efficient, and observable machine learning systems and services.</li> <li>Develop training and inference pipelines for LLM, computer vision, and multimodal AI models.</li> <li>Create model evaluation frameworks, including offline evaluation, online experiments, and user feedback integration.</li> <li>Collaborate with software engineering, data, and product teams to deliver AI-powered features.</li> <li>Deploy, monitor, and maintain machine learning models using containerised and cloud-based infrastructure.</li> <li>Investigate production incidents, improve system reliability, and optimise operational performance.</li> <li>Optimise training and inference costs through batching, quantisation, mixed precision, and GPU resource management.</li> </ul> <strong>Required Skills:-</strong> <ul> <li>Strong proficiency in Python and machine learning frameworks such as PyTorch or TensorFlow.</li> <li>Experience building end-to-end machine learning pipelines, including data preparation, training, evaluation, deployment, and monitoring.</li> <li>Knowledge of MLOps tools such as MLflow, Weights & Biases, DVC, Airflow, or Prefect.</li> <li>Experience with Docker, Kubernetes, containerised deployments, and CI/CD practices.</li> <li>Understanding of GPU optimisation, ONNX, TensorRT, batching, and mixed precision techniques.</li> <li>Familiarity with vector databases, retrieval-augmented generation (RAG), and LLM fine-tuning approaches.</li> <li>Knowledge of observability and monitoring tools such as Prometheus, Grafana, or OpenTelemetry.</li> <li>Strong analytical, problem-solving, and collaboration skills.</li> </ul> <strong>Qualifications & Experience:-</strong> <ul> <li>Bachelor?s or Master?s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.</li> <li>3?5+ years of experience in applied machine learning, AI engineering, or software engineering.</li> <li>Experience developing and deploying production-grade machine learning applications.</li> <li>Understanding of statistics, experimentation, model evaluation, and real-world performance analysis.</li> </ul> <strong>Preferred Attributes:_</strong> <ul> <li>Hands-on experience with LLMs, computer vision, or multimodal AI systems.</li> <li>Ability to balance research innovation with production engineering requirements.</li> <li>Strong ownership mindset and experience working in cross-functional teams.</li> <li>Excellent communication and technical documentation skills</li></ul></p>

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