About FractionAI
FractionAI helps organizations unlock real value from AI, not through hype, but through pragmatic, ROI-driven solutions that work in production. We partner with businesses across industries to design, build, and operationalize AI systems that solve tangible problems and deliver measurable outcomes.
About the Role
We are looking for a 100x AI Solutions Engineer who combines deep technical fluency across the full AI spectrum with sharp business intuition. This is not a research role, it is a hands-on, delivery-oriented position where you will architect, build, and ship production-grade AI solutions that drive real ROI for our clients.
You will work directly with stakeholders to understand business context, identify high-impact opportunities, and translate them into robust, scalable AI systems. The ideal candidate is equally comfortable discussing business strategy with a C-suite executive and writing production Python code at 2 AM.
- Pay: INR 13.5 lacs per annum
- Hours: 7.30PM to 11.30PM IST (with option to go full-time based on performance)
- Location: Remote
What You Will Do
- Design & Deliver AI Solutions: Scope, architect, and build end-to-end AI solutions spanning Business Intelligence, RPA, ML/Predictive Analytics, and Generative AI, always with a production-first, ROI-first mindset.
- Build Agentic Workflows: Design and implement autonomous, multi-step AI agent workflows using tools like n8n, LangChain, LangGraph, CrewAI, and similar agentic orchestration frameworks.
- Engineer Robust Data Pipelines: Stand up and maintain sound data engineering practices, from ingestion and transformation to storage and serving, across major cloud platforms (AWS, GCP, Azure).
- Operationalize ML Models: Own the full MLOps lifecycle: data preparation, feature engineering, model training, evaluation, deployment, monitoring, and retraining.
- Drive Data Quality: Enforce rigorous data science and data cleanliness standards, ensuring that every solution is built on a foundation of trustworthy, well-governed data.
- Engage with Clients: Translate complex technical capabilities into clear business value; work closely with clients to understand their domain, pain points, and success metrics.
Required Qualifications
- Deep, Broad AI Expertise: Proven, hands-on experience across the AI spectrum: Business Intelligence, Robotic Process Automation, Machine Learning / Predictive Analytics, and Generative AI. You understand these domains deeply, not just at a surface level.
- Agentic Workflow Proficiency: Demonstrated experience building agentic, multi-step AI workflows using orchestration tools such as n8n, Make, Zapier (for RPA), as well as agent frameworks like LangChain, LangGraph, AutoGen, or CrewAI.
- Advanced Python Development: Expert-level Python skills with deep familiarity with AI/ML frameworks (LangChain, LangGraph, Hugging Face, scikit-learn, PyTorch/TensorFlow) and production software engineering best practices.
- Business-First Thinking: Ability to understand business context, identify where AI can create the highest leverage, and deliver solutions that are measured by ROI, not novelty.
- Data Engineering Competence: Strong knowledge of cloud data infrastructure (AWS, GCP, Azure), data warehousing, ETL/ELT pipelines, and the trade-offs between different architectural choices.
- Data Science & MLOps Excellence: Deep expertise in data science workflows, data cleanliness, feature engineering, model evaluation, and the operational discipline of MLOps (CI/CD for ML, monitoring, drift detection, retraining pipelines).
Preferred Qualifications
- Healthcare or medical field experience is a MAJOR plus: familiarity with clinical data, HIPAA, HL7/FHIR, medical coding, or health-tech platforms is highly valued.
- Experience in a client-facing setting
- Experience working with early-stage or fast-moving teams where speed, ownership, and adaptability are critical.
- Contributions to open-source AI/ML projects or a strong public portfolio of technical work.
- Familiarity with vector databases (Pinecone, Weaviate, Chroma), RAG architectures, and fine-tuning LLMs.
