Data Engineer Location: India (Hyderabad - Preferred/Remote Flexibility) Type: Full time, Paid The Role Were looking for an AWS focused Data Engineer with hands-on experience building production-grade pipelines at scale. The ideal candidate has 1 to 4 years of experience in data engineering and is excited to solve tough data problems in a fast-paced startup environment. What You Will Do Design, build, and maintain production-level data pipelines that handle terabytes of structured and unstructured data. Own end-to-end ETL/ELT processes, ensuring reliability, scalability, and high availability. Work with AWS services (S3, EC2, RDS, DynamoDB, Athena, Redshift, etc.) to power large-scale data workflows. Collaborate closely with AI/ML teams to support model training, retrieval-augmented generation (RAG), and analytics pipelines. Continuously monitor, optimize, and improve data workflows for performance and cost efficiency. (Nice to have) Use Apache Airflow or similar orchestration tools to manage workflows. What we are looking for 1 to 4 years of professional experience as a Data Engineer (or related role). Proven experience building and running production pipelines at terabyte scale Proficiency with Python and SQL for data engineering. Strong knowledge of AWS cloud services and big data best practices. Bonus: Airflow, Spark, or other orchestration/processing frameworks. A self-starter who thrives in a fast-moving startup environment. About Us: Steps AI is building AIDE, the worlds smartest AI web widget and Chat Platform that powers multi-agent copilots for businesses across industries. Our platform ingests, processes, and analyzes massive datasets across multiple sources to deliver real-time, intelligent insights. Why Join StepsAI: Work on real, production-scale data problems from day one. Be part of a fast-growing AI startup with global reach. Collaborate with a world-class team across AI, data, and product.
AI/ML Engineer (AI Agents) Location: Hyderabad or Remote | Type: Paid | Experience: 0 to 4 years (Hands-on exp with AI Agents, LLMs, or RAG) What We're Looking For: We need an exceptionally strong coder with real-world experience in LLMs, RAG systems, and multi-agent orchestration. You must be curious, fast-learning, and excited to build with a high-performance startup team. Who We Are: Steps AI builds advanced GenAI products that power agentic data retrieval and automation for enterprises. We work on LLMs, multi-agent systems, RAG, LLMOps, and generative AI orchestration. Key Responsibilities: Develop LLM-based agents for enterprise use cases Build scalable RAG pipelines (Naive, Graph, Struct) Implement LLMOps and production-ready ML infrastructure Optimize LLM reasoning with CoT, ToT, GoT prompting Design and deploy multi-agent orchestration with tools like LangGraph, LangChain, or AutoGen What You Must Know: Solid grasp of LLMs (GPT, LLaMA, etc.) and transformer architectures Hands-on experience with RAG systems and agentic workflows Familiarity with Docker, MLOps, cloud deployment (AWS/Azure) Strong coding skills, system design, and software architecture principles Proficiency in Python, PyTorch/TensorFlow, Hugging Face, LangChain, etc. Ability to work collaboratively and own outcomes Why Join Us: Work on cutting-edge AI products, not toy projects Build with serious engineers in a fast-moving startup Mentorship, autonomy, and future full-time opportunity Important: Only apply if you have hands-on experience with LLMs, AI Agents, or RAG. No exceptions. Steps AI We co-create the AI-native enterprise.
Role: SDE What You'll Do Core Web Application Development Build production-grade web applications using Next.js, TypeScript, JavaScript with modern React patterns Develop scalable Python backends using FastAPI for high-performance API services Design and implement RESTful APIs and GraphQL endpoints for frontend-backend communication Architect microservices and serverless functions for modular, scalable applications AI/ML Integration & GenAI Development Integrate LLMs, RAG systems, and AI agents directly into web applications with real-time streaming Build GenAI-powered features including chat interfaces, document processing, and intelligent automation Implement vector search, embedding pipelines, and semantic retrieval for enhanced user experiences Deploy and optimize AI model inference endpoints with low-latency requirements Scalable Systems & Cloud Infrastructure Deploy applications on AWS using Docker, Kubernetes, Lambda, and ECS Implement auto-scaling, load balancing, and monitoring for high-traffic applications Design event-driven architectures with Redis, SQS, and real-time messaging Optimize database performance across PostgreSQL, MongoDB, and vector databases Code Quality & Engineering Excellence Conduct thorough code reviews and maintain clean, testable code with comprehensive documentation Implement CI/CD pipelines with automated testing, deployment, and quality gates Lead architectural decisions for scalable, maintainable full-stack systems Mentor team members on best practices and design patterns Must-Have Qualifications Core Development Experience 1 years in production web application development with proven scalability achievements TypeScript/JavaScript Mastery: Expert-level proficiency with Next.js, React, and modern web standards Python Backend Expertise: Strong experience with FastAPI, async programming, and API design Database Proficiency: Production experience with SQL, NoSQL, and vector databases AI/ML Integration Skills LLM Integration: Hands-on experience building applications with OpenAI, Anthropic, or open-source LLMs RAG Systems: Proven track record implementing retrieval-augmented generation in web applications AI Agents: Experience integrating autonomous agents, tool calling, and workflow orchestration Vector Search: Practical knowledge of embedding models, similarity search, and semantic retrieval Cloud & DevOps AWS Expertise: Production deployments using EC2, Lambda, RDS, S3, and container services Containerization: Proficiency with Docker, Kubernetes, and serverless architectures Monitoring: Experience with CloudWatch, application monitoring, and performance optimization Engineering Rigor Clean Code: Strong adherence to SOLID principles, testing frameworks, and code quality standards Version Control: Advanced Git workflows, branching strategies, and collaborative development Problem Solving: Demonstrated ability to debug complex systems and optimize performance Critical Requirements - High Weightage Open Source & Live Projects Active GitHub portfolio showcasing full-stack applications with AI/ML integration Open source contributions to web frameworks, AI tools, or developer libraries Live project demonstrations - must present production applications you've built and deployed Clean, documented code that demonstrates architectural thinking and best practices Technical Assessment We will critically evaluate your: System design for scalable web applications with AI integration Code quality through GitHub portfolio review and live coding sessions AI/ML integration capabilities through practical project demonstrations Ownership mindset and ability to take end-to-end responsibility for features Nice-to-Have Contributions to Next.js, FastAPI, or AI/ML open-source projects Experience with real-time applications, WebSockets, and streaming data Knowledge of fine-tuning, prompt engineering, and model optimization Technical writing, blogging, or conference speaking experience What We Offer Competitive salary and equity package with rapid growth potential Cutting-edge tech stack with latest AI/ML tools and frameworks Generous learning budget for conferences, courses, and certifications Fully remote with flexible hours and modern development environment Open source time - contribute to projects during work hours Direct mentorship from seasoned engineers and AI researchers Applications without a comprehensive GitHub portfolio and live project demonstration will not be considered. Join Steps AI and build the next generation of intelligent web applications that seamlessly blend cutting-edge AI capabilities with exceptional user experiences.