You will play a leading role in designing, deploying, and scaling production-grade ML systems including large language model (LLM)-based pipelines, AI copilots, and agentic workflows.

This role is ideal for someone who thrives on balancing cutting-edge research with production rigor and loves mentoring while building impact-first AI applications.

Responsibilities :

- Own the full ML lifecycle : model design, training, evaluation, deployment

- Design production-ready ML pipelines with CI/CD, testing, monitoring, and drift detection

- Fine-tune LLMs and implement retrieval-augmented generation (RAG) pipelines

- Build agentic workflows for reasoning, planning, and decision-making

- Develop both real-time and batch inference systems using Docker, Kubernetes, and Spark

- Leverage state-of-the-art architectures : transformers, diffusion models, RLHF, and multimodal pipelines

- Collaborate with product and engineering teams to integrate AI models into business applications

- Mentor junior team members and promote MLOps, scalable architecture, and responsible AI best practices

Ideal Candidate :

- 5+ years of experience in designing, deploying, and scaling ML/DL systems in production

- Proficient in Python and deep learning frameworks such as PyTorch, TensorFlow, or JAX

- Experience with LLM fine-tuning, LoRA/QLoRA, vector search (Weaviate/PGVector), and RAG pipelines

- Familiarity with agent-based development (e.g., ReAct agents, function-calling, orchestration)

- Solid understanding of MLOps : Docker, Kubernetes, Spark, model registries, and deployment workflows

- Strong software engineering background with experience in testing, version control, and APIs

- Proven ability to balance innovation with scalable deployment

- B.S./M.S./Ph.D. in Computer Science, Data Science, or a related field

- Must have a minimum of 5+ years of experience in designing, developing, and deploying Machine Learning / Deep Learning (ML/DL) systems in production

- Must have strong hands-on experience in Python and deep learning frameworks such as PyTorch, TensorFlow, or JAX.

- Must have 1+ years of experience in fine-tuning Large Language Models (LLMs) using techniques like LoRA/QLoRA, and building RAG (Retrieval-Augmented Generation) pipelines.

- Must have experience with MLOps and production-grade systems including Docker, Kubernetes, Spark, model registries, and CI/CD workflows.