Senior AI Engineer

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.