Job
Description
Senior Data Scientist — Gen AI/ML Expert Location: Hybrid — Gurugram Company: Mechademy – Industrial Reliability & Predictive Analytics About Mechademy At Mechademy, we are redefining the future of reliability in rotating machinery with our flagship product, Turbomechanica . Built at the intersection of physics-based models, AI, and machine learning , Turbomechanica delivers prescriptive analytics that detect potential equipment issues before they escalate, maximizing uptime, extending asset life, and reducing operational risks for our industrial clients. The Role We are seeking a talented and driven Senior Data Scientist (AI/ML) with 3+ years of experience to join our AI team. You will play a critical role in building scalable ML pipelines, integrating cutting-edge language models, and developing autonomous agent-based systems that transform predictive maintenance is done for industrial equipment. This is a highly technical and hands-on role, with strong emphasis on real-world AI deployments — working directly with sensor data, time-series analytics, anomaly detection, distributed ML, and LLM-powered agentic workflows . What Makes This Role Unique Work on real-world industrial AI problems , combining physics-based models with modern ML/LLM systems. Collaborate with domain experts, engineers, and product leaders to directly impact critical industrial operations. Freedom to experiment with new tools, models, and techniques — with full ownership of your work. Help shape our technical roadmap as we scale our AI-first predictive analytics platform. Flexible hybrid work culture with high-impact visibility. Key Responsibilities Design & Develop ML Pipelines: Build scalable, production-grade ML pipelines for predictive maintenance, anomaly detection, and time-series analysis. Distributed Model Training: Leverage distributed computing frameworks (e.g. Ray, Dask, Spark, Horovod) for large-scale model training. LLM Integration & Optimization: Fine-tune, optimize, and deploy large language models (Llama, GPT, Mistral, Falcon, etc.) for applications like summarization, RAG (Retrieval-Augmented Generation), and knowledge extraction. Agent-Based AI Pipelines: Build intelligent multi-agent systems capable of reasoning, planning, and executing complex tasks via tool usage, memory, and coordination. End-to-End MLOps: Own the full ML lifecycle — from research, experimentation, deployment, monitoring to production optimization. Algorithm Development: Research, evaluate, and implement state-of-the-art ML/DL/statistical algorithms for real-world sensor data. Collaborative Development: Work closely with cross-functional teams including software engineers, domain experts, product managers, and leadership. Core Requirements 3+ years of professional experience in AI/ML, data science, or applied ML engineering. Strong hands-on experience with modern LLMs (Llama, GPT series, Mistral, Falcon, etc.), fine-tuning, prompt engineering, and RAG techniques. Familiarity with frameworks like LangChain, LlamaIndex , or equivalent for LLM application development. Practical experience in agentic AI pipelines : tool use, sequential reasoning, and multi-agent orchestration. Strong proficiency in Python (Pandas, NumPy, Scikit-learn) and at least one deep learning framework (TensorFlow, PyTorch, or JAX). Exposure to distributed ML frameworks (Ray, Dask, Horovod, Spark ML, etc.). Experience with containerization and orchestration (Docker, Kubernetes). Strong problem-solving ability, ownership mindset, and ability to work in fast-paced startup environments. Excellent written and verbal communication skills. Bonus / Good to Have Experience with time-series data, sensor data processing, and anomaly detection. Familiarity with CI/CD pipelines and MLOps best practices. Knowledge of cloud deployment, real-time system optimization, and industrial data security standards. Prior open-source contributions or active GitHub projects. What We Offer Opportunity to work on cutting-edge technology transforming industrial AI. Direct ownership, autonomy, and visibility into product impact. Flexible hybrid work culture. Professional development budget and continuous learning opportunities. Collaborative, fast-moving, and growth-oriented team culture. Health benefits and performance-linked rewards. Potential for equity participation for high-impact contributors. Note: Title and compensation will be aligned with the candidate’s experience and potential impact. Show more Show less
