Senior AI Engineer

6 days ago

India Srotas Health Full time

Job Description About Srotas Health Srotas Health is an AI-first company building next-generation agentic and generative AI systems. Our core platform, SrotasIQ, leverages proprietary LLMs, multi-agent reasoning, and dynamic orchestration frameworks to deliver adaptive, high-performance AI solutions across diverse real-world use cases. Role Summary We're looking for a Senior AI Engineer who thrives on solving hard problems and scaling ambitious ideas. You'll lead the architecture and development of advanced AI systems from fine-tuning and prompt optimisation to building agentic frameworks and multi-model orchestration. This is a hands-on role for a go-getter and problem solver who's excited to work across model design, infrastructure, and product integration. You'll shape the technical foundation of our platform, guide engineering excellence, and bring cutting-edge research into production. Key Responsibilities - Design, develop, and optimize LLMs, agentic systems, and multi-agent frameworks (LangChain, LlamaIndex, AutoGen, CrewAI, etc.). - Lead fine-tuning, prompt tuning, and token optimization efforts to improve model accuracy, latency, and cost efficiency. - Build scalable RAG pipelines, vector search layers, and reasoning agents for production use cases. - Architect AI-driven workflows using transformer-based models, embedding strategies, and real-time inference pipelines. - Collaborate cross-functionally to convert ambiguous challenges into deployable AI capabilities. - Mentor engineers and drive best practices across ML Ops, experimentation, and performance benchmarking. - Continuously evaluate and integrate advancements in LLM architectures, reinforcement learning, and agentic systems. - Must-Haves - 4-6 years of experience in applied AI/ML engineering, including production-level deployments. - Experience with agentic frameworks (LangChain, LlamaIndex, CrewAI, AutoGen, Semantic Kernel, Dust, etc.). - Experience building custom agent frameworks beyond existing libraries. - Exposure to LLM fine-tuning, RLHF, LoRA, or distillation techniques. - Knowledge of multi-modal agents, combining text, vision, and structured data reasoning. - Open-source contributions or published work in applied AI or agent systems. - Familiarity with token optimization, quantization, and inference acceleration techniques. - Contributions to open-source AI/ML projects or publications in applied AI. - Deep understanding of LLMs, transformers, embeddings, and RAG architectures. - Proficiency in Python, TensorFlow/PyTorch, and backend frameworks such as FastAPI. - Hands-on experience with vector databases (e.g., Milvus, Weaviate, Pinecone) and LLM orchestration tools. - Proven ability to lead and deliver complex technical projects with ownership and agility. Compensation - Opportunity to build and scale core components of a next-gen AI platform. - Competitive salary with equity participation. - Remote-first and flexible work culture. - Freedom to experiment, innovate, and publish applied AI work. - A culture that rewards initiative, creativity, and execution.

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