Description :
Mandatory Requirements :
- Minimum 5+ years of experience in designing, developing, and deploying Machine Learning / Deep Learning systems in production.
- Strong hands-on experience with Python and deep learning frameworks such as PyTorch, TensorFlow, or JAX.
- At least 1 year of experience in fine-tuning Large Language Models (LLMs) using LoRA / QLoRA and building RAG (Retrieval-Augmented Generation) pipelines.
- Practical experience with MLOps and production-grade systems including Docker, Kubernetes, Spark, model registries, and CI / CD workflows.
Preferred Requirements :
Prior experience in open-source GenAI contributions, applied LLM / GenAI research, or large-scale production AI systems.B.S. / M.S. / Ph.D. in Computer Science, Data Science, Machine Learning, or a related technical field.Role and Responsibilities :
Lead end-to-end machine learning lifecycle activities including model design, training, evaluation, deployment, and scaling.Build and manage production-ready ML pipelines with CI / CD, monitoring, testing, and model drift detection.Fine-tune LLMs and implement RAG-based systems for enhanced retrieval and reasoning.Design and develop agentic workflows for planning, reasoning, and decision-making.Create both real-time and batch inference systems using Docker, Kubernetes, and Spark.Utilize advanced architectures such as transformers, diffusion models, RLHF, and multimodal systems.Work collaboratively with engineering and product teams to integrate AI models into production applications.Mentor junior team members and promote best practices in scalable ML systems, MLOps, and responsible AI.Ideal Candidate Profile :
Proven experience in production-grade ML / DL system design and deployment.Expertise in LLM fine-tuning, RAG pipelines, and vector search tools (e.g., Weaviate, PGVector).Strong software engineering background with knowledge of APIs, version control, and testing.Familiarity with agent-based development approaches (e.g., ReAct agents, orchestration, function-calling).Ability to balance research innovation with production scalability.Additional advantage for open-source contributions, GenAI research, or large-scale applied systems.Location Options : Mumbai / Bengaluru / Hyderabad / Gurugram
Experience Required : 5 - 12 Years
Working Days : 5 Days / Week (Hybrid - 3 Days WFO)
Notice Period : Maximum 60 Days
(ref : hirist.tech)