Technical Manager Applied AI Operations CloudBerry Technologies

6.0 - 10.0 years

0 Lacs

karnataka

On-site

You have a Bachelor's Degree in Computer Science and possess 6 to 10 years of experience. The job is based in Bangalore. As a Technical Manager for Applied AI Operations, your primary responsibility will be to oversee the operational delivery of AI/ML solutions. You will ensure reliable, scalable, and efficient support for production AI systems. Your role will involve managing client engagements, collaborating with architecture and DevOps teams, and driving innovations in AI/ML operations, particularly in the context of LLM-based solutions. Your key responsibilities will include overseeing operational support and performance for deployed AI models (e.g., GPT, Claude, BERT) in live production environments. You will guide the use of retrieval-augmented generation (RAG) architectures, vector databases (FAISS, Pinecone), and orchestration frameworks (LangChain, LlamaIndex). Additionally, you will implement AI monitoring tools and observability solutions (e.g., Arize, Evidently) to ensure accuracy, latency, drift detection, and model health. Acting as the technical point of contact for client-facing delivery of AI/ML services, you will ensure client satisfaction, uptime, and ongoing optimization. Collaborating with solution architects, DevOps engineers, and AI/ML teams, you will manage infrastructure, CI/CD pipelines, and compliance across deployments. You will also be responsible for driving the development of operational accelerators and reusable components to standardize and optimize LLM/AI model deployments. Identifying and implementing automation opportunities across AI workflows and support functions to improve reliability and reduce manual interventions will be a key part of your role. To excel in this role, you are required to have at least 6 years of experience in technology management, including 2 years of managing production AI/ML systems and teams. You should possess a strong working knowledge of large language models, vector databases, and RAG-based architectures. Familiarity with MLOps best practices, pipelines, containerization (Docker/Kubernetes), and cloud-native AI tooling (Azure OpenAI, AWS Bedrock, GCP Vertex AI) is essential. Hands-on experience with LangChain, LlamaIndex, Arize, Evidently, MLFlow, or similar operational tools is a plus. You must have proven leadership skills to manage multidisciplinary teams, deliver client-facing projects, and balance priorities under deadlines. Strong stakeholder communication skills are also required, with the ability to translate complex AI operations into business value.,

Posted 1 month ago

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