AI Engineer

Role overview Develop and deploy machine-learning solutions that address business problems, from data preparation and model development through production monitoring. The work spans predictive and generative AI, including retrieval-augmented applications and agent workflows, and involves collaboration with engineering, data, and client teams.

Responsibilities - Lead model development end to end, including data ingestion, preprocessing, training, evaluation, deployment, and monitoring. - Build generative AI solutions such as RAG applications, agent workflows, MCP servers, and conversational agents. - Work with data engineering teams on pipelines, feature stores, and orchestration. - Integrate models into production APIs, microservices, and cloud deployments; optimize for scale, robustness, performance, and cost. - Monitor production behavior, including drift, bias, fairness, and reliability, and address issues. - Document model designs, experiments, data provenance, and technical rationale; explain results to clients and stakeholders.

Requirements - Bachelor’s or master’s degree in computer science, engineering, data science, or a related field. - At least three years of hands-on AI/ML model development and deployment experience. - Strong Python skills and experience with machine-learning frameworks such as TensorFlow, PyTorch, or scikit-learn. - Experience with language-model development or fine-tuning, vector databases, RAG, and agent workflows. - Experience deploying models to production through APIs or microservices and monitoring their operation.

Nice to have - Familiarity with frameworks such as LangChain or LangGraph and tools for model operations. - Exposure to speech-to-text or text-to-speech systems, computer vision, time-series modeling, or multimodal AI. - Experience with MCP, agent SDKs, or real-time voice platforms.

Benefits and work setup - The role is listed as remote in India.