Associate Process Manager

Accountabilities: Design, develop, optimize, and deploy deep learning models for enterprise AI initiatives. Build and enhance LLM-based solutions using Retrieval-Augmented Generation (RAG), agent orchestration, and context or memory management. Integrate and fine-tune LLM APIs from providers such as OpenAI, Anthropic, Google Gemini, Mistral, and similar platforms. Work with vector databases such as Pinecone, Chroma, and FAISS to support intelligent search and retrieval workflows. Develop scalable and modular AI pipelines using frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, or comparable technologies. Conduct experimentation, model evaluation, performance tuning, and optimization using PyTorch, TensorFlow, JAX, or related frameworks. Collaborate with data engineering and infrastructure teams to support reliable AI deployments in AWS, GCP, Azure, or other cloud environments. Use Docker and Kubernetes for model containerization, deployment, scaling, and lifecycle management. Apply prompt engineering, context optimization, and LLM fine-tuning techniques where appropriate. Document technical solutions, experiments, implementation decisions, and results clearly for stakeholders and team members. Contribute to multidisciplinary AI initiatives and help identify opportunities to improve model performance, scalability, and business value. Requirements Master's degree, M.Tech, or Ph.D. in Computer Science, Artificial Intelligence, Data Science, or a related technical discipline. Strong proficiency in Python and experience with modern AI development libraries. Solid understanding of machine learning and deep learning algorithms, including their practical implementation. Hands-on experience with AI and deep learning frameworks such as PyTorch, TensorFlow, or JAX. Practical knowledge of LLM technologies and AI orchestration approaches. Experience working with vector databases and retrieval-based AI architectures. Understanding of cloud computing platforms such as AWS, GCP, or Azure. Familiarity with Docker, Kubernetes, and containerized application deployment. Experience with LLM APIs, RAG architectures, agentic workflows, or comparable enterprise AI solutions. Experience with prompt engineering, LLM fine-tuning, and context optimization is an advantage. Strong analytical and problem-solving skills, with excellent attention to detail. Ability to work effectively with multidisciplinary teams across AI, data engineering, infrastructure, and business functions. Strong written and verbal communication skills, including the ability to document technical concepts and solutions clearly. Benefits Full-time opportunity with a focus on enterprise artificial intelligence and machine learning. Remote working opportunity within India, with potential alignment to Mumbai, Pune, or Chandigarh. Hands-on exposure to deep learning, LLMs, RAG, agent orchestration, and modern AI frameworks. Opportunity to work with leading LLM providers and vector database technologies. Exposure to AWS, GCP, Azure, Docker, Kubernetes, and enterprise-scale AI deployment. Opportunity to contribute to innovative AI initiatives with practical business applications. Collaborative environment involving AI, data engineering, infrastructure, and other multidisciplinary teams. Professional growth through hands-on experience with rapidly evolving AI technologies. Opportunity to work on scalable, production-oriented AI solutions rather than purely experimental projects. Equal opportunity work environment focused on diverse talent and professional development.